Journal of Intelligent & Fuzzy Systems - Volume 36, issue 1

Authors: Cui, Wenhua | Ye, Jun | Shi, Lilian

Article Type: Research Article

Abstract: To describe the linguistic neutrosophic information with uncertain variables in the complex decision-making problems, this paper originally defines the concept of a linguistic neutrosophic uncertain number (LNUN). The LNUN consists of three uncertain linguistic variables given by three neutrosophic linguistic numbers to describe the truth, falsity, and indeterminacy linguistic information independently. Then, both the basic operational laws of LNUNs and the score and accuracy functions of LNUNs are presented for the ranking of LNUNs. After that, two operators including a LNUN weighted arithmetic averaging (LNUNWAA) operator and a LNUN weighted geometric averaging (LNUNWGA) operator are developed to aggregate LNUN information. …Based on the aggregation operators, a novel multiple attribute group decision-making (MAGDM) method is established under a LNUN environment. Finally, an example of investment decision is illustrated to demonstrate the application and the effectiveness of the developed method. Show more

Keywords: Linguistic neutrosophic uncertain number, linguistic neutrosophic uncertain number weighted arithmetic averaging operator, linguistic neutrosophic uncertain number weighted geometric averaging operator, score function, accuracy function, multiple attribute group decision-making, neutrosophic linguistic number

DOI: 10.3233/JIFS-18331

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 1, pp. 649-660, 2019

Authors: Gholam, Atiyeh Mashhadi | Ezzati, Reza | Allahviranloo, Tofigh

Article Type: Research Article

Abstract: In the present study, first, we introduce an iterative method based on quadrature formula for solving two-dimensional nonlinear fuzzy Fredholm integral equations (2DNFFIE). Then, we present error estimation and the numerical stability analysis for the proposed method. Finally, to show the efficiency of the proposed method, supporting examples are also provided.

Keywords: Two-dimensional nonlinear integral equation, quadrature iterative method, convergence analysis, numerical stability analysis

DOI: 10.3233/JIFS-171104

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 1, pp. 661-674, 2019

Authors: Vijayan, A.T. | Ashok, S.

Article Type: Research Article

Abstract: This work compares the performance of three different models of neural networks in predicting the intermediary pose of a robot end effector for visual servoing tasks. Robotic applications in complicated and complex workspaces benefit from the use of non-touching sensor technology like vision. Visual feedback control of a two camera robotic system combines the advantages of global and local visibilities of a fixed camera guiding the robot and end effector camera achieving convergence for pick and place tasks. Neural networks replace the control law for the visual guidance for targets initially not in the field of view of the eye-in-hand …camera. Visual features collected by the eye-to-hand camera and the robot pose form input for the three types of networks, Multilayer Perceptron Neural Network (MLPNN), Radial Basis Function Neural Network (RBFNN) and Elman Neural Network (ENN). The robot moves to the predicted pose, favorable for switching to Image Based Visual Servoing (IBVS) limiting the number of discrete events. Simulation studies and experimentation with an ABB make robot are performed for drawing conclusions regarding the network performance. Show more

Keywords: Visual servoing, MLP, RBF, ENN, condition number

DOI: 10.3233/JIFS-171475

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 1, pp. 675-688, 2019

Authors: Guo, Jifa | Wang, Zhongliang | Duan, Yanyan

Article Type: Research Article

Abstract: The division of the internal structure and external space of geographical entities is the premise of the analysis of topological relations and directional relations. Currently, most division methods are crisp, which does not conform to human cognitive habits. Occasionally, users want to integrate their meaning by vague natural language when they understand spatial scenarios; however, natural language suffers from uncertainty due to the effects of individual characteristics and the context of environmental factors. To handle uncertainties in spatial conceptions of regional features, the semantic spatial partitioning model for regions based on computing with words is proposed. The structure of a …region is divided into several parts using fuzzy logic according to people’s cognitive habits, and then, a detailed direction model of regions is proposed. The proposed method is applied to understand a remote sensing dataset, and the results show that the proposed method can enrich the understanding of images while conforming to human cognitive habits. Show more

Keywords: IT2FS, semantic spatial partitioning, CWW, spatial relation, remote sensing

DOI: 10.3233/JIFS-171662

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 1, pp. 689-707, 2019

Authors: Liang, Baohua | Wang, Lin | Liu, Yong

Article Type: Research Article

Abstract: The choice of attribute significance is the most important step of attribute reduction algorithm. Information entropy is a method of calculating the importance of attributes. Due to the fact that information view only takes the size of knowledge granularity into account rather than measures the importance of attributes objectively and comprehensively this paper begins with putting forward the definition of approximate boundary accuracy based on algebra view. Afterwards, this paper proposes two concepts of relative information entropy and enhanced information entropy based on the definition of relative fuzzy entropy, which has obvious magnification effect. Then, two new methods of attribute …reduction are proposed by incorporating approximate boundary precision into relative information entropy and enhanced information entropy, so that the choice of the importance of the attribute is more objective and comprehensive. Finally, it will analyze and compare the classification accuracy of each kind of algorithm by using the SVM classifier and ten-fold crossover method, and analyze the influence of outliers on the effect of the algorithm. Through experimental analysis and comparison, it can be concluded that the attribute reduction based on improved entropy is feasible and effective. Show more

Keywords: Attribute reduction, approximate boundary accuracy, relative information entropy, enhanced information entropy, blend entropy

DOI: 10.3233/JIFS-171989

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 1, pp. 709-718, 2019

Authors: Feng, Xiangqian | Liu, Qi | Wei, Cuiping

Article Type: Research Article

Abstract: QUALIFLEX (QUALItative FLEXible multiple criteria method) is one of useful outranking methods for analyzing multiple criteria decision problems because of its flexibility with respect to the information of cardinal and ordinal. This paper developed a probabilistic linguistic QUALIFLEX method with possibility degree comparison method for dealing with group decision making problems, in which the evaluation information of alternatives are expressed by hesitant fuzzy linguistic sets and standard weights are partially known. Note that it is more reasonable using probabilistic linguistic term sets (PLTSs) to represent the whole viewpoint of group, which are got by combining HFLTSs. Firstly, the possibility degree …formula for comparing PLTSs is proposed and their properties are discussed. Then, the concordance/ discordance index, the weighted concordance/ discordance index, and the comprehensive concordance/discordance index are defined by using the possibility degree comparison method. The optimal ordering order of the alternatives is obtained based on the study of all possible permutations of alternatives and the establishment of linear programming models. Finally, an application study and a comparative analysis with two cases are conducted to indicate the feasibility, reasonability and applicability of the proposed methodology. Show more

Keywords: Multi-criteria decision making, probabilistic linguistic term sets, QUALIFLEX, possibility degree

DOI: 10.3233/JIFS-172112

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 1, pp. 719-730, 2019

Authors: Wu, Yao | Yao, Daojin | Xiao, Xiaohui | Guo, Zhao

Article Type: Research Article

Abstract: Passive dynamic walking (PDW) has attracted much research attention due to its humanoid and energy efficient gaits. However, walking control of the passivity-based biped robot inspired by PDW still remains a challenge, for PDW is sensitive to disturbances. An walking controller is essential for practical passivity-based biped robots in real environments. This paper presents a deep reinforcement learning (DRL) controller based on deep Q network for planar passivity-based biped robot, to learn policies directly from inputs for bipedal walking task. First, the intelligent controller using deep Q network is trained, with PDW as reference trajectory. The learning experience from PDW …could be helpful to implement a natural looking and energy-efficient gait. Then the trained deep Q network is utilized as the walking controller. Simulation results show that the DRL controller based on deep Q network makes the planar biped robot walk against original value disturbance, on different slope, level ground and varying slopes. The controller this paper presented could be used to improve the versatility of the passivity-based biped robot. Show more

Keywords: Biped robot, deep Q network, deep reinforcement learning, passive dynamic walking, Q learning

DOI: 10.3233/JIFS-172180

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 1, pp. 731-745, 2019

Authors: Anuradha, | Singh, Akansha | Gupta, Gaurav

Article Type: Research Article

Abstract: The ever increasing advances in the field of biotechnology and health information sciences have led to large electronic health records (EHRs), which in turn contains important genetic and clinical information. Machine learning and data mining techniques are playing vital and indispensible efforts to intelligently convert available data into useful information for effective medical diagnosis. But, designing effective prediction and diagnosis techniques for diabetes mellitus (DM) are getting more attention than ever before. Thus, a novel fuzzy rule miner (ANT_FDCSM) derived from ant colony meta-heuristic for diagnosis of diabetic patients has been proposed in this paper. A few important improvements have …been suggested to improve the performance of traditional ant colony optimization induced decision tree classifier. The first improvement is done to optimize search space of construction graph by employing a novel approach for optimal split point selection. Secondly, to compute heuristic information, a hybrid node split measure (SW_FDCSM) is presented. SW_FDCSM is a combination of attribute significance weight (SW) with a new fuzzy variant (Fuzzy_DCSM) of famous distinct class split measure (DCSM). The improvements have been proposed to generate comprehensive rule set while maintaining good accuracy, sensitivity and specificity count. A 10 fold cross validation (10-FNo) is applied on Pima Indian Diabetes (PID) data set to validate the performance of the proposed fuzzy rule miner (ANT_FDCSM). Show more

Keywords: Feature extraction, fuzzy classifiers, fuzzy clustering, decision tree classifier, medical diagnosis, bio inspired algorithms

DOI: 10.3233/JIFS-172240

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 1, pp. 747-760, 2019

Authors: Wang, Gui Ping | Yang, Jian Xi

Article Type: Research Article

Abstract: Feature extraction is an important preprocessing step in many research areas. For anomaly detection, the purpose of feature extraction lies in not only extracting the most important features hidden in the datasets, but also discriminating different classes of samples. The latter is usually referred to as discriminative ability. The data collected from production systems usually do not follow Gaussian distribution. They may correspond to nonlinear mixture of independent components. In order to cope with non-Gaussian data and implement nonlinear feature extraction, this article proposes a feature extraction algorithm based on Supervised Independent Component Analysis with Kernel (termed SKICA). SKICA first …adopts Kernel Principle Component Analysis (KPCA) to whiten the datasets. Further, by virtue of the within-cluster scatter matrix derived from Linear Discriminate Analysis (LDA), SKICA extends Independent Component Analysis (ICA) to supervised situation by introducing within-cluster information into solving independent components. The latter improvement makes SKICA obtain the independent components more beneficial to separating different classes of samples. In order to quantitatively measure discriminative ability of the feature extraction algorithms involved in experiments, this article defines three kinds of average square distance. This article conducts experiments on artificial datasets, Cloud datasets, and KDD Cup datasets to evaluate the effectiveness of SKICA. The experimental results show that SKICA outperforms several popular supervised feature extraction algorithms, including LDA, LDA with kernel (KDA), and supervised ICA (SICA). Show more

Keywords: Feature extraction, anomaly detection, independent component analysis (ICA), supervised, kernel method

DOI: 10.3233/JIFS-17749

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 1, pp. 761-773, 2019

Authors: Yang, Haodong | Zhang, Jun | Li, Shuohao | Luo, Tingjin

Article Type: Research Article

Abstract: Human action recognition in naturalistic videos is an important task with a broad range of applications. Recently, the encoder-decoder framework based on attention mechanism has been applied to action recognition. Although such conventional methods reach state-of-the-art, they always face a bottleneck of distinguishing similar actions. To solve this problem, we propose a novel recurrent attention convolutional neural network (RACNN), which incorporates convolutional neural networks (CNNs), long short-term memory (LSTM) and attention mechanism. Inspired by the composition of the action, the pre-action and the result of action might be important parts of an action, we introduce bi-direction LSTM with hierarchical structure. …Additionally, the separated spatial-temporal attention is employed into our method. Furthermore, we find that incorporating spatio-temporal features extracted from three-dimensional CNNs (3DCNNs) and RGB features can enhance the relationship mined in each frame. Our comprehensive experimental results on two benchmark datasets, i.e., HMDB51 and UCF101, verify the effectiveness of our proposed methods and show that our proposals can significantly outperform the current state-of-the-art methods. Show more

Keywords: Action recognition, bi-direction hierarchical LSTM, spatial-temporal attention

DOI: 10.3233/JIFS-18209

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 1, pp. 775-786, 2019