Journal of Intelligent & Fuzzy Systems - Volume 37, issue 5

Authors: Haktanır, Elif | Kahraman, Cengiz

Article Type: Research Article

Abstract: Hypothesis tests are a statistical decision-making tool for testing if a hypothesized parameter value is supported by the sample data or not. Vagueness and impreciseness in the sample data require fuzzy techniques to be employed in the analysis. These techniques can be based on intuitionistic fuzzy sets, hesitant fuzzy sets, type-2 fuzzy sets, neutrosophic sets, or spherical fuzzy sets. In this paper, Z-fuzzy numbers are used to capture the vagueness in the sample data and develop Z-fuzzy hypothesis testing. A Z-fuzzy number is represented by a restriction function that is usually a triangular or trapezoidal fuzzy number and a reliability …function representing the confidence level to the restriction function. Illustrative examples for left and right sided hypothesis testing and sensitivity analyses are presented. Show more

Keywords: Z-fuzzy number, hypothesis testing, statistical decision making, restriction function, reliability function

DOI: 10.3233/JIFS-182700

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6545-6555, 2019

Authors: Liu, Yuan | Xie, Min | Zhu, Jinjin | Hao, Jingjing

Article Type: Research Article

Abstract: Natural monotonic linguistic language is widely used to express experts’ uncertain subjective appraisal opinion, such as “More than”, “At least”, “Less than” and “At most”, which reveals explicit information about performance range and implicit information about his hiding preference on a linguistic scale. A novel computational method for monotonic hesitant fuzzy linguistic terms is developed to transfer experts’ uncertain appraisal information to decision-making data, which can systematically consider and mine expert’s obvious explicit and hidden implicit appraisal information. Specially, the comprehensive meanings of monotone decreasing and increasing hesitant fuzzy linguistic terms are investigated, in which both explicit and implicit appraisal …information are explored to reveal its actual meaning. Additionally, Weibull distribution functions with three parameters are fitted considering the comprehensive meaning of monotone increasing appraisals, which is determined by a multi-objective programming model following ABC classification method. Symmetry principle is employed to confirm the expression of monotone decreasing appraisals, which are transferring from monotone increasing appraisals with same length of domain field. Moreover, feasibility analysis is explored to show the influence of parameters on decision-making precision. Finally, a numerical study is conducted to show the feasibility and advantage of the new method, which can effectively improve the precision of computational transfer by comparing to previous method. Show more

Keywords: Monotonic hesitant fuzzy linguistic term set, implicit appraisal information, Weibull distribution, ABC analysis

DOI: 10.3233/JIFS-182754

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6557-6571, 2019

Authors: Rashid, Junaid | Adnan Shah, Syed Muhammad | Irtaza, Aun

Article Type: Research Article

Abstract: Medical and health text documents pose a challenge for data handling and retrieving the relevant and meaningful documents. Automatically retrieval of significant knowledge with a better understanding of medical and health documents is a challenging task. One popular approach for thematically understand the medical and health text documents and finding the topics from these documents is topic modeling. In this research, we propose a novel topic modeling approach Fuzzy k-means latent semantic analysis (FKLSA) by using the fuzzy clustering. Our method generates local and global term frequencies through the bag of words (BOW) model. Principal component analysis is used for …removing high dimensionality negative impact on global term weighting. Previous work shows that in medical and health documents redundancy issue has a negative impact on the quality of text mining. Therefore, the main achievement of FKLSA is the handling of the redundancy issue in medical and text documents and discover semantically more precise topics. FKLSA is socially utilized for finding the themes from medical and health text corpus. These topics are further used for text classification and clustering tasks in text mining. Experimental results show that FKLSA performs better than LDA and RedLDA for redundant corpora. FKLSA’s time performance is also stable with an increase in number of topics and thus better than LDA and LSA on a big twitter heath dataset. Quantitative evaluations of the real-world dataset for health and medical documents show that FKLSA gives a higher performance as compared to state-of-the-art topic models like Latent Dirichlet allocation and Latent semantic analysis. Show more

Keywords: Topic modeling, bag-of-words, term weighting, fuzzy k-means, principal component analysis

DOI: 10.3233/JIFS-182776

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6573-6588, 2019

Authors: Meng, Xiao-Li | Gong, Liu-Tang | Yao, Jen-Chih

Article Type: Research Article

Abstract:   Evaluating the performances of a set of entities called decision making units (DMUs) which convert multiple inputs into multiple outputs has long been considered as a difficult task because one is dealing with complex economics. This work proposes an inequality approach to evaluate the performances of DMUs. Inequalities consist of expressions of the production possibility set and the line segments joining the evaluated DMU to the positive output-axes. However, in real-world application involving performance measurement, inputs and outputs are often imprecise and fluctuated. In this case, a fuzzy inequality approach is proposed to evaluate the performances. What is more, …fuzzy relative efficiency is dependent upon the number of solutions. Furthermore, the minimal element is used to distinguish the fuzzy relative efficient DMUs. Finally, two numerical examples are used to illustrate the fuzzy approach and compare the results with those obtained with alternative fuzzy approaches. Show more

Keywords: Fuzzy data envelopment analysis, Fuzzy inequality, The production possibility set, The positive output-axes, Minimal element

DOI: 10.3233/JIFS-182823

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6589-6600, 2019

Authors: Sun, Zhe | Zheng, Jinchuan | Man, Zhihong | Wang, Hai | Shao, Ke | He, Defeng

Article Type: Research Article

Abstract:  This paper presents a novel adaptive fuzzy sliding mode (AFSM) control scheme for a vehicle steer-by-wire (SbW) system. Initially, the dynamics of the SbW system are described by a second-order differential equation where the Coulomb friction and the self-aligning torque are treated as external disturbances. Furthermore, an AFSM controller is designed for the SbW system, which utilizes an adaptive law to estimate both the Coulomb friction and the self-aligning torque, a sliding mode control component to deal with the parametric uncertainties and unmodeled dynamics, and a fuzzy strategy to strike a good balance between the chattering-alleviation and the tracking precision. …The stability of the control system is verified in the sense of Lyapunov, and the selection of control parameters is provided in detail. Lastly, experiments are carried out under various road conditions. The experimental results demonstrate that the developed AFSM controller possesses superiority in terms of higher tracking accuracy, stronger robustness and a better balance between the control precision and smoothness in comparison with a conventional sliding mode (CSM) controller and a boundary layer-based adaptive sliding mode (BLASM) controller. Show more

Keywords: Adaptive fuzzy sliding mode (AFSM), steer-by-wire (SbW), vehicle, self-aligning torque

DOI: 10.3233/JIFS-182824

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6601-6612, 2019

Authors: Farzaneh, Ghorbani | Mohsen, Afsharchi | Vali, Derhami

Article Type: Research Article

Abstract: This paper proposes a novel multi-agent unit commitment model under Smart Grid (SG) environment to minimize the demand satisfaction error and production cost. This is a distributed solution applicable in non-deterministic environments with stochastic parameters intending to solve Distributed Stochastic Unit Commitment (DSUC) problem. We use multi-agent reinforcement learning (RL) in which agents learn as independent learners to cooperatively satisfy the demand profile. The learning mechanism proceeds using a reward signal, which is given based on the performance of the entire system as well as the impact of the joint action of the agents. The learning agent utilizes a novel …multi-agent version of Fuzzy Least Square Policy Iteration (FLSPI) as a model-free RL algorithm to approximate Q-function. Based on this approximation, the agent makes the best decision to achieve the goals while considering the constraints governing the system. Uncertainty sources in our definition of the problem are fluctuations in the predicted demand function, random productions of clean energy generators and the possibility of accidental failure in power generators. Training for one time interval (i.e. one season or one year) consisting of several time intervals (i.e. days) can be simultaneously conducted by one trial in our method. We have conducted our experiment in two different frameworks. These frameworks are defined based on the problem complexity in terms of the number of generators, the uncertainties in the environment and the system constraints. The results show that the learning agent learns to satisfy the demand profile as well as other constrains. Show more

Keywords: Multi-agent reinforcement learning, Stochastic Unit Commitment, fuzzy approximation

DOI: 10.3233/JIFS-182879

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6613-6628, 2019

Authors: Ghasemi Nejad, S.M. | Borzooei, R.A.

Article Type: Research Article

Abstract: In this paper, the notions of (semi) topological basic algebra and (semi) topological implication basic algebra are introduced, along with evaluating their properties. Then, different operations are defined based on basic algebras and the relationship between semicontinuity and continuity of operations is considered. In addition, the separation axioms on (semi) topological basic algebras are investigated by considering some conditions implying that a (semi) topological basic algebra becomes a T i - space, for i  ∈ {0, 1, 2}. In the sequel, some relations between (weak) ideals and (weak) filters of basic algebras are obtained and (left) topological (implication) basic algebra …is constructed by using the concepts of (weak) filters, which is a zero dimensional, normal, disconnected, locally compact and completely regular (left) topological space. Further, the notion of quotient basic algebras are presented along with evaluating the interaction of topological basic algebras and topological quotient basic algebras. Finally, it is proved that there is an implication basic algebra IB * with cardinality n  + 1 and filter F *  = F  ∪ {z * }, which z * ∉ IB for any implication basic algebra IB of cardinality n and filter F . Accordingly, it is proved that there is at least one nontrivial regular and normal topological implication basic algebra of cardinality n . Show more

Keywords: Basic algebra, topological basic algebra, continuous, separation axioms, topological quotient basic algebras, 54A05, 54A10, 03G12, 03G25

DOI: 10.3233/JIFS-182947

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6629-6644, 2019

Authors: Jian, Jie | Zhan, Nian | Su, Jiafu

Article Type: Research Article

Abstract: In the field of engineering economy, engineering investment selection is a common problem, where the preference information is usually intuitionistic and fuzzy. To deal with the consistency and integrity of the information in the selection process, the aim of this article is to extend the superiority and inferiority ranking method and use the interval-valued intuitionistic fuzzy theory, where the individual evaluation values and the weights information of criteria and decision-makers are all described by interval-valued intuitionistic fuzzy numbers. First, some concepts of interval-valued intuitionistic fuzzy set are introduced. Then, the interval-valued intuitionistic fuzzy superiority and inferiority ranking (IVIF-SIR) method is …developed. Moreover, an engineering investment selection model based on IVIF-SIR method is investigated. Finally, an illustration of choosing investment alternatives is used to prove the developed approach and a comparative study is also use to demonstrate the effectiveness. Show more

Keywords: SIR method, multiple criteria group decision making, interval-valued intuitionistic fuzzy Set

DOI: 10.3233/JIFS-190001

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6645-6653, 2019

Authors: Frizzo Stefenon, Stéfano | Silva, Marcelo Campos | Bertol, Douglas Wildgrube | Meyer, Luiz Henrique | Nied, Ademir

Article Type: Research Article

Abstract: Reliability in the electric power system is fundamental to the development of society, for which rapid and accurate methods of fault identification are required. Faults in distribution insulators are hardly visible and the fault behavior is often intermittent, which makes its diagnosis a difficult task. Fault diagnosis with the ultrasound equipment has been used efficiently since this equipment is directional and not influenced by sunlight. However, the interpretation of the signal generated by this equipment requires an experienced operator and they are also susceptible to provide false diagnostics. The use of advanced algorithms to classify electrical system conditions has been …proven as a great alternative to automate operator decisions. This article proposes the use of artificial intelligence algorithms such as single-layer and multilayer Perceptron for classification of distribution insulators conditions. The use of artificial neural networks for insulator classification is an innovative subject. Some researchers have already worked on partial discharges however not specifically for fault classification in insulators of distribution networks. The application of this technique can make the inspection of the electrical system automated and, in this way, more accurate and efficient. The results of the analysis showed that the application of signal linearization technique joint with artificial intelligence is a good alternative to locate faults in insulators. Show more

Keywords: Fault identification, artificial neural network, grid inspection, classification, insulators

DOI: 10.3233/JIFS-190013

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6655-6664, 2019

Authors: Akl, Ahmed | El-Henawy, Ibrahim | Salah, Ahmad | Li, Kenli

Article Type: Research Article

Abstract: Hyperparameter optimization is a crucial step in the implementation of any machine learning model. This optimization process includes regularly modifying the hyperparameter values of the model in order to minimize the testing error. A deep neural learning model hyperparameter optimization process includes optimizing both the model parameters and architecture. Optimizing a model’s parameters involves deciding the values of parameters, such as learning rate and batch size. Optimizing architectural hyperparameters includes deciding the shape of the deep neural learning model, i.e. , the number of layers of individual types and the number of neurons in a certain layer. The state-of-the-art hyperparameter …optimization methods don’t optimize the position of the hyperparameter within the model architecture. In this work, we study the effect of changing a hyperparameter within the deep learning model architecture. Thus, we propose an arch itectural pos ition opt imization (ArchPosOpt ) method for model architectural hyperparameter optimization. ArchPosOpt extends three different hyperparameter optimization techniques, namely grid search, random search, and Tree-structured Parzen Estimator (TPE), to include a new dimension of hyperparameter optimization problem – the hyperparameter position. We show through a set of experiments that the position of the hyperparameters does matter for model performance as well as the hyperparameter values. Show more

Keywords: Deep neural networks, hyperparameter optimization, CNN, architectural optimization, hyperparameter position

DOI: 10.3233/JIFS-190033

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6665-6681, 2019

Authors: Fayaz, Muhammad | Ullah, Israr | Shah, Abdul Salam | Kim, DoHyuen

Article Type: Research Article

Abstract: Intelligent optimized energy management and prediction model in electric vehicles received attraction of the researchers in the last couple of years. Several techniques and models have been proposed in the literature for optimized energy management and control, but the trade-off between occupant comfort index and the energy consumption is still a significant challenge to the research community. In this paper, we have proposed a model based on learning to optimization and learning to control for user comfort maximization and efficient energy consumption. The proposed model is comprised of three layers; prediction module, learning to optimization module and learning to control …module. In the prediction module, we have used the Kalman filter for noise removal and prediction of environmental parameters. In learning to optimization module, the bat algorithm has been used for user comfort maximization and energy consumption minimization. Furthermore, we have used the learning module with optimization module in order to tune the user preferences parameters in the comfort index formula used in the bat optimization algorithm. Likewise, the learning module has been used with the conventional fuzzy logic controller in order to improve its performance. In the conventional fuzzy logic controller, the membership functions boundaries are usually determined through hit and trial method, and once the membership functions are determined, they remain fixed for the entire process. In the learning to control module, the membership functions tuning is carried out. The membership functions are continuously tuned to get effective results. Experimental results indicate that the proposed method performs better as compared to the conventional methods and achieves improved user comfort with reduced energy consumption. Show more

Keywords: Energy optimization, energy consumption, user comfort, bat algorithm, electric vehicles, learning to control

DOI: 10.3233/JIFS-190095

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6683-6706, 2019

Authors: Saritha, S. | Santhosh Kumar, G.

Article Type: Research Article

Abstract: The spatial colocation problem is totally different from the traditional association rule problem, as it operates on spatial data and not on conventional transaction data. In this work, a spatial colocation mining framework is proposed that mines spatial colocation of image-objects present in images using a tensor factorization approach. The framework takes in image data directly, tensorize it and perform the mining task, thus eliminating the need of converting into a transaction based approach. An interestingness measure called, spatial dominance is also proposed in this work. This measure is an indicator of the prevalence of the mined colocation pattern. Algorithms …are designed in this framework, first to map the classified pixels as members of image-objects, which is a pre-stage before mining and second to find spatial colocation patterns. Experiment results are provided to show the strength of the spatial colocation mining algorithm. Show more

Keywords: Data mining, spatial colocation, tensors, image-objects

DOI: 10.3233/JIFS-190122

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6707-6716, 2019

Authors: Ziari, Shokrollah | Bica, Alexandru Mihai

Article Type: Research Article

Abstract: In this paper, an iterative numerical method has been developed to solve nonlinear fuzzy Volterra integral equations based on three-point quadrature formula. The error estimation of the method is obtained based on Lipschitz condition and in order to confirm the yielded theoretical results, we perform the iterative method on some numerical examples.

Keywords: Nonlinear fuzzy Volterra-Hammerstein integral equations, Iterative numerical method, L-Lipschitz fuzzy functions

DOI: 10.3233/JIFS-190149

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6717-6729, 2019

Authors: de Jesús Rubio, José | Garcia, Enrique | Ochoa, Genaro | Elias, Israel | Cruz, David Ricardo | Balcazar, Ricardo | Lopez, Jesus | Novoa, Juan Francisco

Article Type: Research Article

Abstract:  An unscented Kalman filter can be applied for the experimental learning of the solar dryer for oranges drying and the greenhouse for crop growth to know better the processes and to improve their performances. The contributions of this document are: a) an unscented Kalman filter is designed for the learning of nonlinear functions, b) the unscented Kalman filter is applied for the experimental learning of the two mentioned processes.

Keywords: Unscented Kalman filter, greenhouse, solar dryer, experimental learning

DOI: 10.3233/JIFS-190216

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6731-6741, 2019

Authors: Poornappriya, T.S. | Durairaj, M.

Article Type: Research Article

Abstract: The prompt enhancement of Telecom turned to be a vibrant and economical industry, which comprises an intrinsically great perspective for customer churn, requiring exact churn prediction models. In recent times, there has been phenomenal responsiveness in the development of feature selection methods for a large number of datasets. Through this research work, a High Relevancy and Low Redundancy (HRLR) approach by consuming Vague Set (VS) has proposed for selecting the subset of features from the features set. This proposed method is based on the Minimum Redundancy and Maximum Relevancy (MRMR) approach by using Vague Set. The proposed HRLR-VS method is …based on the filtered approach feature selection, where the features are selected only when the measure of feature-class relevancy is maximized and a measure of feature-feature redundancy is minimized. The collaboration of similarity measures and ranking algorithms are prepared by utilizing the vital notions of Vague Sets information energies by Information Gain, Gain Ratio, and Chi-Square methods. The projected approach has been employed with the Particle Swarm Optimization for probing the best feature subset. Further, it measures the efficacy of the projected approach HRHL-VS for telecommunication dataset. The performance metrics like Accuracy, Kappa Statistics, True Positive Rate, Precision, F-Measure, Recall, MAE, RRSE, RMSE and RAE are considered in this paper for evaluating the proposed HRLR-VS method. The proposed HRRL-VS method has compared with existing literature approaches like mRMR and FCBF. From the result obtained in this paper, the proposed HRLR-VS method better results in all aspects for selecting the feature subset in telecommunication dataset. Show more

Keywords: Feature Selection, Vague Set, Information Gain, Gain Ratio, Chi-Square, Particle Swarm Optimization, Euclidean Distance, Cosine Similarity, Pearson’s Correlation Coefficient

DOI: 10.3233/JIFS-190242

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6743-6760, 2019

Authors: Zerhari, Btissam | Lahcen, Ayoub Ait | Mouline, Salma

Article Type: Research Article

Abstract: Attribute and class noises are the two important sources of Corruptions (noise) contained in real-world datasets which may deteriorate data interpretation and accuracy. Class noise has potentially serious negative impacts compared to attribute noise, however, the existing major class noise detection methods are not able to address this problem efficiently. To overcome issues related to detection and the elimination of class noise, we suggest a new noise filtering approach able to identify and remove class noise, called Multi-Iterative Partitioning Class Noise Filter (MIPCNF). Since there is no single filter that consistently outperforms its counterparts in all database types and in …different levels of noise, our approach relies on an algorithm in which several rounds of class noise detection are performed on different partitions of the data using several classifiers. Therefore, we use different filtering strategies: iterative noise filter, partitioning filter and ensemble-based filter. The experimental results, on 14 real-world datasets, and statistical analysis, show that our method is not only overcoming the higher noise but also over-performing latest class noise detection and elimination strategies in different levels of noise. Show more

Keywords: Class noise, Noise Detection, Noise Elimination, Partitioning Filter, Large Data

DOI: 10.3233/JIFS-190261

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6761-6772, 2019

Authors: Maheshwari, Karan | Joseph Raj, Alex Noel | Mahesh, Vijayalakshmi G.V. | Zhuang, Zhemin | Rufus, Elizabeth | Shivakumara, Palaiahnakote | Naik, Ganesh R.

Article Type: Research Article

Abstract: In today’s world, there have been lots of unique optical character recognition systems. One drawback of these systems is that they cannot work effectively on natural scene images where the text is not only subject to different orientations, lightning, and background but can be of multiple scripts as well. The paper, proposes a state of the art algorithm to detect texts of different dialects and orientations in an image. The whole text detection pipeline is divided into two parts. First, extraction of probable text regions in an image is performed based on a combination of statistical filters, which results in …a high recall. These regions are then fed to an Artificial Neural Networks (ANN) based classifier which classifies whether the proposed regions are text or non-text, which increases the overall precision. The validity of the algorithm is verified on the most challenging bilingual text detection dataset MSRA-TD500 and a promising F1 score of 0.67 is reported. Show more

Keywords: Text detection, entropy and variance filters, invariant moments, artificial neural networks, bilingual text detector

DOI: 10.3233/JIFS-190339

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6773-6784, 2019

Authors: Vanitha, V. | Krishnan, P.

Article Type: Research Article

Abstract: An e-learning system offering a personalised learning path will be vastly appealing to the learners. Adaptive techniques when employed in e-learning can sustain the interest and motivation of the learners and help them to complete the enrolled courses successfully. In addition, it would improve their performance and thus, enhance the overall learning experience. Personalisation takes into consideration the characteristics of the individual learner and the diversity in his/her needs. The main challenge is finding a match between these individual characteristics and the sequence of the learning content. It is a complex task to implement as it involves selection of the …appropriate material from a vast amount of the available learning materials. It is a challenge to perform this process manually as it requires both technical savvy and pedagogical skills. In this paper, a stigmergy model is proposed, which was applied to build a customised learning path. The aim was to provide personalisation that satisfied the needs of an individual in a widely heterogeneous e-learning environment. Compared with the traditional teaching method, this tailored learning path, generated using the proposed approach, shows promise and was found to enhance the performance of the learners. Show more

Keywords: Learning path, learning content sequence, personalised E-learning, ant colony optimisation, curriculum sequencing

DOI: 10.3233/JIFS-190349

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6785-6800, 2019

Authors: Ajeena Beegom, A.S. | Rajasree, M.S.

Article Type: Research Article

Abstract: Scientific workflow applications include a set of tasks, which have complex inter dependencies with each other, along with a large number of parallel tasks. The problem of scheduling such application tasks involves careful decisions on determining the sequence in which it can be processed, causing high impact on the cost of execution and makespan (execution time), when executed on a cloud computing system. Achieving optimal schedule, which can optimize both of these objectives while keeping the dependencies between tasks intact is a real challenge. In this work, a non-dominated sorting based particle swarm optimization approach to find an optimal schedule …for workflow applications in cloud computing systems is proposed. A graph is used to represent tasks in the workflow and the dependencies among tasks. The optimization problem is modelled using integer programming formulation, subject to capacity and dependency constraints among tasks and Virtual Machines (VM). Simulation studies and result comparison with other representative algorithms in the literature shows that the proposed algorithm is promising. Show more

Keywords: Cloud computing, workflow scheduling, non-dominated sorting, particle swarm optimization, pareto-optimality

DOI: 10.3233/JIFS-190355

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6801-6813, 2019

Authors: Naz, Farah | Kamran, Muhammad | Mehmood, Waqar | Khan, Wilayat | Alkatheiri, Mohammed Saeed | Alghamdi, Ahmed S. | Alshdadi, Abdulrahman A.

Article Type: Research Article

Abstract: The figurative language involving sarcasm on social networks is evolving the way how the humans use computers to communicate. Consequently, artificial intelligence techniques are applied in various scenarios to make the social networking more intelligent - for instance, identification of figurative language. Identifying both literal and non-literal meaning is not easy for a machine and it is hard even for people. Therefore, novel and exact frameworks ready to identify figurative languages are important. In sarcasm detection, this is even more challenging because sarcasm changes the polarity of an evidently positive or negative expression into its inverse. To maintain a …distance for a sarcastic message being comprehended in its unintended actual meaning, in micro-blogging sites, for example messages on Twitter, sarcasm is frequently set apart with a hashtag for example, ’#sarcastic’, '#sarcasm', ’#not’ etc. Moreover, the customer reviews may also contain some element of sarcasm. To contribute to this area, we gathered the data of tweets and reviews from Twitter, thesarcasmdetector.com, and Kaggle and proposed a mechanism for detecting sarcasm automatically using a classifier. A detailed experimental study was also conducted to evaluate the proposed mechanism. The results of this study were quite promising and proved the effectiveness of our approach. Show more

Keywords: Computational semantics, sarcasm detection, intelligent social networking, understanding uncertainty

DOI: 10.3233/JIFS-190596

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6815-6828, 2019

Authors: Singh, Abhilash | Sharma, Sandeep | Singh, Jitendra | Kumar, Rahul

Article Type: Research Article

Abstract: Wireless sensor networks (WSNs) found application in many diverse fields, starting from environment monitoring to machine health monitoring. The sensor in WSNs senses information. Sensing and transmitting this information consume most of the energy. Also, this information requires proper processing before final usages. This paper deals with minimising the redundant information sensed by the sensors in WSNs to reduce the unnecessary energy consumption and prolong the network lifetime. The redundant information is expressed in terms of the overlapping sensing area of the working sensors set. A mathematical model is proposed to find the redundant information in terms of the overlapping …area. A combined meta-heuristic approach is used to achieve the optimal coverage, and the effect of the overlapping area is considered in the objective function to reduce the amount of redundant information sensed by the working sensors set. Improved genetic algorithm (IGA) and Binary ant colony algorithm (BACA) are used as meta-heuristic tools to optimise the multi-objective function. The objective was to find the minimum number of sensors that cover a complete scenario with minimum overlapping sensing region. The results show that optimal coverage with the minimum working sensor set is achieved and then by incorporating the concept of overlapping area in the objective function, sensing of redundant information is further reduced. Show more

Keywords: Improved genetic algorithm, Binary ant colony algorithm, Redundant information, Objective function, Overlapping area

DOI: 10.3233/JIFS-190605

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6829-6839, 2019

Authors: Yu, Qian | Dian, Songyi | Li, Yong | Liu, Jiaxin | Zhao, Tao

Article Type: Research Article

Abstract:  Since the non-singleton fuzzy logic controllers (NFLCs) can effectively reflect the uncertainty brought by the inputs, they are used for balance control and position control of the mobile two-wheeled self-balancing robot (MTWSBR) in this paper. The similarity between the inputs and the antecedent fuzzy sets as the firing strength, that is, the similarity-based non-singleton fuzzy logic controller (Sim-NFLC), is proposed to deal with the problem of information loss caused by the standard non-singleton fuzzy logic controller (Sta-NFLC) in terms of the interaction of the inputs and antecedents. A comparative study among singleton fuzzy logic controllers (SFLCs), Sta-NFLCs and Sim-NFLCs, and …interval type-2 fuzzy logic controllers (IT2FLCs) and general type-2 fuzzy logic controllers (GT2FLCs) are also shown. The simulation results show that the performance of Sim-NFLCs is better than that of SFLCs and Sta-NFLCs. The similarity-based general type-2 fuzzy logic controller (Sim-NGT2FLC) gets the best performance in handling the input uncertainty. Show more

Keywords: Mobile two-wheeled self-balancing robot, non-singleton general type-2 fuzzy logic controllers, similarity measure, firing strength

DOI: 10.3233/JIFS-190683

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6841-6854, 2019

Authors: Elakkiya, R. | Vanitha, V.

Article Type: Research Article

Abstract:  Vision-based Sign Language Recognition has been an open research problem since decades. Many existing methods for sign recognition works well under restricted laboratory conditions but failed to support real-time scenarios because extraction of manual and non-manual movements with constantly changing shapes of signs are considered as tedious problem in machine vision and machine learning. To overcome these shortcomings, an interactive real time class level gesture similarity based sign recognition using Artificial Neural Network is presented in this paper. The method uses the sign images and starts with enhancing the image quality. The quality enhancement is performed by equalizing the histograms …of luminance and contrast. The features of hand as subunits from quality improved image have been extracted by template matching techniques. Extracted features are used to generate neural network and trained with different class of signs. The classification is performed by measuring the class level gesture similarity measure towards each class of signs and images. Based on the measure estimated, the method classifies the image and sign. The result produced to the user has been iterated based on the actions provided by the user. The method is capable of iterating the result and recognition till the user gets satisfied. The method produces higher accuracy in sign recognition and reduces the false ratio. Show more

Keywords: ANN, sign recognition, gesture similarity, CLGSM, template matching, interactive systems

DOI: 10.3233/JIFS-190707

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6855-6864, 2019

Authors: Enayattabr, M. | Ebrahimnejad, A. | Motameni, H. | Garg, H.

Article Type: Research Article

Abstract: Researchers have studied several different types of directed shortest path (SP) problems under fuzzy environment. However, few researchers have focused on solving this problem in an interval-valued fuzzy network. Thus, in order to light these, we investigate a generalized kind of the SP problem under interval-valued fuzzy environment namely all pairs shortest path (APSP) problem. The main contributions of the present study are fivefold: (1) In the interval-valued fuzzy network under consideration, each arc weight is represented in terms of interval-valued fuzzy number. (2) We seek the shortest weights between every pair of nodes in a given interval-valued fuzzy network …based on a dynamic approach. (3) In contrast to most existing approaches, which provide the shortest path between two predetermined nodes, the proposed approach provides the interval-valued fuzzy shortest path between every pair of nodes. (4) Similarly to the competing methods in the literature, the proposed approach not only gives the interval-valued fuzzy weights of all pairs shortest path but also provides the corresponding interval-valued fuzzy APSP. (5) The efficiency of the proposed approach is illustrated through two applications of APSP problems in wireless sensor networks and robot path planning problems. Show more

Keywords: Shortest path problem, dynamic programming, interval-valued fuzzy numbers, wireless sensor network

DOI: 10.3233/JIFS-190711

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6865-6877, 2019

Authors: Liu, Zhiyong | Jia, Fangyun | Wang, Ali | Luo, Lianhe

Article Type: Research Article

Abstract: This study investigated fault information estimation and diagnosis using a novel approach based on an integrated fault estimator and state estimator for an electric motor in coal mine. The proposed scheme uses a self-constructing fuzzy unscented Kalman filter (UKF) system to simultaneously estimate the system state and approximate the fault information. To achieve this, a generalized linear discrete-time system of the electric motor in coal mine without faults was first transformed into an equivalent standard state-space system with faults. Then, the self-constructing fuzzy UKF system was designed in order to obtain the fault information. According to fault information obtained fault …detection experiments based on fuzzy clustering were performed with the proposed scheme and the fault feature parameters required for fault isolation were determined. Finally, the scheme was applied to an electric motor in coal mine to demonstrate the effectiveness of the proposed fault estimation and diagnosis approach. Results of the simulation illustrate the effectiveness of the proposed method. Show more

Keywords: Self-constructing fuzzy system, unscented Kalman filter (UKF), state estimation, fault information, electric motor, coal mine

DOI: 10.3233/JIFS-190755

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6879-6890, 2019

Authors: Liu, Shiqin

Article Type: Research Article

Abstract: Uncertain differential equation with jumps is a type of uncertain differential equation driven by both Liu process and uncertain renewal process. Many concepts of stability for uncertain differential equation with jumps have been investigated. This paper presents a concept of exponential stability for uncertain differential equations with jumps, and gives a sufficient and necessary condition for the linear uncertain differential equation with jumps being exponentially stable. The relationships among stability in measure, stability in in mean, stability in p -th moment, and almost sure stability for uncertain differential equation are also discussed.

Keywords: Uncertain differential equation, exponential stability, uncertainty theory

DOI: 10.3233/JIFS-190771

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6891-6898, 2019

Authors: Jia, Heming | Li, Yao | Lang, Chunbo | Peng, Xiaoxu | Sun, Kangjian | Li, Jinduo

Article Type: Research Article

Abstract: Grasshopper optimization algorithm (GOA) is proposed for imitating grasshopper’s behavior in nature, which has the disadvantages of slow convergence speed and unbalanced exploration and exploitation, etc. Therefore, an algorithm called GOA_jDE, which combines GOA and jDE is proposed to improve the optimization performance. Firstly, the adaptive strategy is introduced into DE to improve the global search ability in the proposed algorithm. Secondly, the combination of jDE and GOA greatly improves the convergence efficiency while maintaining the population diversity. Finally, it can be observed in the work that the proposed algorithm improves the convergence speed and calculation precision. In the subsequent …experiments, 14 well-known test benchmark functions are used to compare the advantages of GOA_jDE. The experimental results illustrate that the performance of proposed algorithm has significant improvement, which also proves the feasibility and effectiveness. Considering the complexity of engineering problems, three classical engineering design problems (tension/compression spring, welded beam, and pressure vessel designs) are used to evaluate the performance of the proposed algorithm. In addition, the classical engineering design results proves the merits of this algorithm in solving real problems with unknown search spaces. Show more

Keywords: Grasshopper optimization algorithm, differential evolution, self-adapting based algorithm, hybrid optimization, functions optimization

DOI: 10.3233/JIFS-190782

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6899-6910, 2019

Authors: Feng, Jianshe | Jia, Xiaodong | Zhu, Feng | Yang, Qibo | Pan, Yubin | Lee, Jay

Article Type: Research Article

Abstract: Maintenance Scheduling and Routing (MS&R) is critical for the offshore wind farm to reduce maintenance cost. Although different models are proposed, the turbine operating conditions and the forecasted wind resources in the maintenance horizon are still less accounted in these current models. To address this issue, this research proposes a novel mathematical model to optimize the MS&R problem by highlighting the significance of turbine production loss (PL) before and during maintenance activities. In the proposed methodology, the PL term takes the most up-to-date wind turbine power curve and the forecasted wind resources as model inputs. Subsequently, a novel Genetic Algorithm …(GA) solver is designed to minimize the PL of wind turbines together with the technician salaries and the transportation costs. The outcome of the proposed model gives a detailed maintenance plan with maintenance schedules, vessel routes, technician assignments, and cost breakdowns. Validation of the proposed model is implemented on real-world data collected from an offshore wind farm with several 4 MW wind turbines. The result demonstrates the effectiveness and superiority of the proposed method, and some practical findings are also summarized in the conclusions. Show more

Keywords: Maintenance scheduling and routing, offshore wind farm, production loss, genetic algorithm

DOI: 10.3233/JIFS-190851

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6911-6923, 2019

Authors: Esfahani, Hassan Noori | Saadati, Reza

Article Type: Research Article

Abstract: We study the problem of Aleksandrov in fuzzy n -normed spaces and prove that every surjective fuzzy function preserving unit n -distance is affine, and thus is a fuzzy n -isometry. Finally, we show that every fuzzy function preserving two fuzzy unit n -distances confirmers the result of Benz theorem when target space is fuzzy n -strictly convex.

Keywords: Aleksandrov problem, f-n-UDPP, f-n-NLS, fuzzy n-isometry, f-β-n-distance, f-n-strictly convex, 54H20, 46L05, 11Y50

DOI: 10.3233/JIFS-190852

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6925-6935, 2019

Authors: Naeem, Khalid | Riaz, Muhammad | Peng, Xindong | Afzal, Deeba

Article Type: Research Article

Abstract:  We study, in this paper, some notions related to Pythagorean fuzzy soft sets (PFSSs) along with their algebraic structures. We present operations on PFSSs and their peculiar characteristics and elaborate them with real life examples and tabular representation to develop the affluence of linguistic variables based on Pythagorean fuzzy soft (PFS) information. We present an application of PFSSs to the multi-criteria group decision-making (MCGDM ) related to site selection, accompanied by Algorithm and flow chart. We develop PFS TOPSIS method and PFS VIKOR method as extensions of the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) and …VIKOR (Vlse Kriterijumska Optimizacija Kompromisno Resenje) respectively. Finally, we rendered an application in stock exchange investment problem and tackled it by both PFS TOPSIS and PFS VIKOR methods. Show more

Keywords: Pythagorean fuzzy soft sets, linguistic variable, MCGDM, aggregation operator, TOPSIS, VIKOR

DOI: 10.3233/JIFS-190905

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6937-6957, 2019

Authors: Borzooei, R.A. | Kim, Hee Sik | Kouhestani, N.

Article Type: Research Article

Abstract: In this paper we define the notions of a norm and a prenorm on BL -algebras and we give some methods for constructing them. We introduce (pre)metrics on BL -algebras and find some connections between them and (pre)norms. Using prenorms, we find some conditions that a BL -algebra become a metric space. Finally, we show that if there exists a prenorm on a BL -algebra A , then for any filter F in A , there are topologies U and V on A and on A /F …respectively such that ( A , U ) and ( A / F , V ) are (semi)topological BL -algebras. Show more

Keywords: (Semi)topological BL-algebra, prenorm, norm, premetric, metric space

DOI: 10.3233/JIFS-190988

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6959-6969, 2019

Authors: Ye, Qinghao | Tu, Daijian | Qin, Feiwei | Wu, Zizhao | Peng, Yong | Shen, Shuying

Article Type: Research Article

Abstract:  Traditional clinical diagnostic aid systems for medical images are facing challenges of reliability and interpretability. Artificial intelligence has the potential to bring driving changes to disease diagnosis methods through rapid traversal of medical images and efficient classification. However, the application of artificial intelligence in the field of medical image still faces challenges. Our method combines the multiple modalities of attention which consider the most discriminative part in the images. The proposed classification method is tested on the microscopic image dataset with 40 leukocyte categories, which achieves top-1 accuracy of 84.21% and top-5 accuracy of 99.44% during the testing procedure. And …experiments on the dermoscopic image dataset show that our method has good generalization ability across multiple imaging modalities. Show more

Keywords: Leukocyte, image processing, deep learning, dual attention, few-shot learning

DOI: 10.3233/JIFS-191000

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6971-6982, 2019

Authors: Liu, Bingsheng | Chen, Yuan | Shen, Yinghua | Yin, Xianfei

Article Type: Research Article

Abstract: Investigating clusters of experts is an interesting topic in the large-group decision-making (LGDM) problem, since being familiar with patterns (groups) of experts is beneficial to some other actions needed for decision-making (e.g., reconciliation of opinions derived from different expert groups). However, not too much attention has been paid to expert clustering in the LGDM problem under a linguistic environment. Besides, it seems that only the decision information is utilized to group experts while the auxiliary (outside) knowledge (e.g., expertise and occupation) about these experts has not been fully considered during the clustering process. To address this issue, this study proposes …a hybrid method integrating outside knowledge about experts with practical preference information under the interval-valued linguistic environment to cluster experts. The method consists of four elements: pre-clustering of experts according to the given knowledge, the optimization model to transform the interval-valued 2-tuple linguistic (IV2TL) decision information, the data envelopment analysis-discriminant analysis (DEA-DA) model to deal with a two-cluster issue, and iterative clustering based on the DEA-DA model to cluster experts into multiple clusters. The feasibility and validity of the proposed method are illustrated with a real-world example. A comparison with the maximal tree clustering method in the linguistic environment is provided. Show more

Keywords: Large-group decision-making (LGDM), interval-valued 2-tuple linguistic (IV2TL) representation model, outside knowledge, expert clustering

DOI: 10.3233/JIFS-191092

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6983-7001, 2019

Authors: Liu, Zhengmin | Li, Lin | Wang, Xinya | Liu, Peide

Article Type: Research Article

Abstract:  A normal wiggly hesitant fuzzy set (NWHFS) is viewed as a powerful and useful tool to dig the potential uncertainty of decision makers (DMs) in the process of expressing their preferences, which can be regarded as an extended form of the traditional hesitant fuzzy set (HFS). The NWHFSs have the ability of both reserving the original hesitant fuzzy information completely and exploring potential fuzziness of DMs, which assist the DMs in advancing the decision-making efficiency and derive the reasonable ranking orders finally. To fully exert the strengths of the combined power average and Muirhead mean operators, based on the proposed …distance measure of normal wiggly hesitant fuzzy elements (NWHFEs), we extend the power Muirhead mean (PMM) to the normal wiggly hesitant fuzzy environment and develop the normal wiggly hesitant fuzzy PMM (NWHFPMM) and its weighted form included. After that, several representative cases and attractive properties of the proposed normal wiggly hesitant fuzzy operators are investigated in depth. Finally, a novel MADM method for solving normal wiggly hesitant fuzzy decision-making problems is developed, then a numerical example is performed to analyze the strengths of our proposed method, which in the way of comparing with other existing studies. Show more

Keywords: Normal wiggly hesitant fuzzy sets, power average, Muirhead mean, multiple-attribute decision making

DOI: 10.3233/JIFS-191110

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 7003-7023, 2019

Authors: Wei, Cun | Wu, Jiang

Article Type: Research Article

Abstract: Recently, the TODIM (an acronym in Portuguese for Interactive Multi-criteria Decision Making) method, which can characterize the decision makers’ psychological behaviors under risk, has been introduced to handle multiple attribute group decision making (MAGDM) problems. Moreover, the probabilistic linguistic term sets (PLTSs) are effective tool for depicting uncertainty of the MAGDM problems. In this paper, we extend the TODIM method to the MAGDM with PLTSs. Firstly, the definition, comparative method and distance of PLTSs are simply introduced, and the steps of the classical TODIM method for MAGDM problems are presented. Then, on the basis of the conventional TODIM method, the …extended TODIM method is proposed to deal with MAGDM problems in which the attribute values are depicted in the PLTSs, and its significant characteristic is that it can fully consider the decision makers’ bounded rationality which is a real action in decision making. Finally, a numerical example for green supplier selection is proposed to verify the developed approach and its practicality and effectiveness. Show more

Keywords: Multiple attribute group decision making (MAGDM), probabilistic linguistic term sets (PLTSs), TODIM method, Prospect theory, green supplier selection

DOI: 10.3233/JIFS-191164

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 7025-7037, 2019

Authors: Satpathy, Sambit | Prakash, M. | Debbarma, Swapan | Sengupta, Aditya S. | Bhattacaryya, Bidyut K.

Article Type: Research Article

Abstract: Nowadays there are lots of fatal diseases are growing at a rapid rate. We consider about four primary diseases like jaundice, diabetes mellitus, yellow fever, and cholera. In this paper, we design a novel method with the help of fuzzy and FPGA system for prediction multiple diseases in a rural area. Association rule mining technique helps to define fuzzy rules, which implemented on both Spartan3-E and Artix-7 FPGA kit. Due to this implementation, it is easier to design a cost-effective and portable system for multi-disease prediction. The innovation lies in design a low power FPGA and meticulousness method for identification, …prediction of four fatal diseases. The whole plan has tested on Xilinx and Cadence tool for generating RTL model and Layout design. Show more

Keywords: Fuzzy logic, medical diagnosis, association rule mining, Artix-7, FPGA

DOI: 10.3233/JIFS-181577

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 7039-7046, 2019

Authors: Nasseri, Seyed Hadi | Khatir, Mehdi Ahmadi

Article Type: Research Article

Abstract: This paper organizes a two-stage DEA models by taking into account undesirable output with fuzzy stochastic data. A normal distribution with fuzzy component adopted for inputs, intermediate outputs, desirable and undesirable outputs. We propose, finally, a linear and feasible model in deterministic form. To achieve this aim, a possibility-probability approach is applied on a reform of two-stage DEA models occupied with undesirable outputs. A case study in the banking industry is presented to exhibit the efficacy of the procedures and demonstrate the applicability of the proposed model. AMS Mathematics Subject Classification (2000): 62A86, 90C70

Keywords: Data envelopment analysis, Two-stage, Undesirable output, Fuzzy random variable, Efficiency score

DOI: 10.3233/JIFS-181684

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 7047-7057, 2019

Authors: Li, Huimin | Su, Limin | Cao, Yongchao | Lv, Lelin

Article Type: Research Article

Abstract: Pythagorean fuzzy sets (PFSs), as an extension of intuitionistic fuzzy sets (IFSs) for dealing with uncertainty information, have attracted considerable attention in the decision-making area. The Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method is one of the most popular decision-making approaches. In the TOPSIS method, the desired alternative should have not only the shortest distance from the positive ideal solution, but also the farthest distance from the negative ideal solution. Similarity measures play an important role in assessing the degree between ideal and proposal alternatives in decision-making. Thus, this paper aims to provide an extended …TOPSIS by developing new similarity measures with PFSs and applying it to multi-criteria decision-making (MCDM) problems. The main contributions of this paper are as follows: (1) development of three new similarity measures with PFSs, and investigation of their properties; (2) extension of the TOPSIS method based on the proposed similarity measures; and (3) establishment of a Pythagorean fuzzy decision-making method using the improved TOPSIS method. A case study on the selection of a project delivery system is conducted to show the applicability of the presented approach. Show more

Keywords: Pythagorean fuzzy sets, Similarity measure, Pythagorean Fuzzy TOPSIS method, Project delivery system

DOI: 10.3233/JIFS-181690

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 7059-7071, 2019

Authors: He, Xingxing | Li, Yingfang | Du, Limin | Qin, Keyun

Article Type: Research Article

Abstract:  The measure of the similarity between intuitionistic fuzzy sets (IFSs) is an important topic in IFSs theory. In this paper, we propose two computational formulae for similarity measures on IFSs based on a quaternary function called intuitionistic fuzzy equivalence. We first propose the concept of intuitionistic fuzzy equivalence. Then we give a computational formula for intuitionistic fuzzy equivalencies (i.e., Eq. (1)), which is obtained from combining dissimilarity functions and fuzzy equivalencies. Based on Eq. (1), we obtain two computational formulae for similarity measures on IFSs. The first one is obtained by aggregating Eq. (1). The second one is obtained by …respectively aggregating the numerator and the denominator of Eq. (1). We also examine some properties of the proposed similarity measures on IFSs. Finally, we make a comparison between the proposed similarity measures on IFSs and those existing ones in the literature through several counter-intuitive cases. Show more

Keywords: Intuitionistic fuzzy sets, Similarity measures, Dissimilarity functions, Fuzzy equivalencies, Intuitionistic fuzzy equivalencies

DOI: 10.3233/JIFS-181739

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 7073-7086, 2019

Authors: Qu, Guohua | An, Qianying | Qu, Weihua | Deng, Feihu | Li, Tianjiao

Article Type: Research Article

Abstract: The aim of this paper is to investigate dual hesitant fuzzy multiple attribute decision making problems where the attribute values provided by experts are expressed in dual hesitant fuzzy element, and the attribute weight is unknown. We present a new method to derive the weights of attributes and rank the preference order of alternatives based on bidirectional projection models. We introduce some notions, such as some dual hesitant fuzzy operational laws, dual hesitant fuzzy ideal point, the modules of dual hesitant fuzzy elements, and distance between two intuitionistic trapezoidal fuzzy numbers. We also introduce the cosine of the included angle …between the attribute value vectors of each alternative and the dual hesitant fuzzy ideal point. Then we establish the bidirectional projection models to measure the similarity degrees between each alternative and the dual hesitant fuzzy ideal point, and the Jaynes maximum information entropy method is used to determine the attribute weight. Furthermore, we establish a nonlinear optimization model to obtain the weight vector of attributes. Finally, we illustrate the developed projection models with a numerical example. Show more

Keywords: Dual hesitant fuzzy set, bidirectional projection measure, attribute weight

DOI: 10.3233/JIFS-181970

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 7087-7102, 2019

Authors: Shi, Gang | Sheng, Yuhong

Article Type: Research Article

Abstract: Backward uncertain differential equation is a specical type of differential equation driven by a Liu process. There are some concepts of stability such as stability in measure, stability in mean, stability in p -th moment, stability moment exponential and almost sure stability of backward uncertain differential equations have been proposed. As a supplement, this paper gives a concept of stability in uncertain distribution of backward uncertain differential equation. Some sufficient conditions for a backward uncertain differential equation being stable in uncertain distribution are provided. In addition, this paper further discusses their relationships among stability in uncertain distribution, stability in uncertain …measure, stability in mean and stability in p -th moment. Last, this paper discusses some examples to illustrate the theoretical considerations. Show more

Keywords: Uncertain distribution, uncertain process, backward uncertain differential equation, stability in distribution

DOI: 10.3233/JIFS-182877

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 7103-7110, 2019