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