Journal of Intelligent & Fuzzy Systems - Volume 37, issue 1

Authors: Zhou, Linna | Shen, Leping | Yang, Chunyu

Article Type: Research Article

Abstract:  This paper presents a disturbance-observer based sliding mode control (SMC) for fuzzy singularly perturbed systems (SPSs) with uncertainties and disturbances. Firstly, we designed a linear sliding surface. The sliding surface parameter matrix is determined by solving linear matrix inequalities (LMIs). The stability of the sliding mode is proved by a Lyapunov function. Secondly, a disturbance observer is designed to estimate the disturbance, and the obtained disturbance estimate is incorporated in the design of SMC. The reachability condition under the fuzzy SMC law is shown to be satisfied. Finally, simulation results show the feasibility and effectiveness of the proposed control method.

Keywords: Singularly perturbed systems (SPSs), sliding mode control (SMC), linear matrix inequality (LMI), T-S fuzzy model

DOI: 10.3233/JIFS-181995

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 1, pp. 1055-1064, 2019

Authors: Thao, Nguyen Xuan | Smarandache, Florentin

Article Type: Research Article

Abstract: Pythagorean fuzzy sets are an extension of the intuitionistic fuzzy sets, and it also overcomes the limitations of the intuitionistic fuzzy sets. The entropy of a Pythagorean fuzzy set (PFS) is a measure of uncertainty related to the PFS. In this article, we exploit the concept of probability for defining the fuzzy entropy of Pythagorean fuzzy sets as an extension of the fuzzy entropy of Intuitionistic fuzzy sets (IFSs). Compared to some previous measures, the new measure is simpler, closer to the statistical significance and it reflects better fuzzy properties. After that, we give some numerical examples to compare our …proposed entropy measure to some existing entropy of Pythagorean fuzzy sets. The results on numerical examples show that the proposed entropy measures seem to be more reliable for presenting the degree of fuzziness of a PFS and/or IFS. We also proposed a COPRAS multi-criteria decision-making method with weights calculated based on the proposed new entropy measure. The illustrated numerical example shows that the calculated results according to the proposed new method are similar to the calculation results according to some other existing methods. Show more

Keywords: Pythagorean fuzzy sets, entropy measures, COPRAS method

DOI: 10.3233/JIFS-182540

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 1, pp. 1065-1074, 2019

Authors: Yong, Rui | Zhu, Aqin | Ye, Jun

Article Type: Research Article

Abstract:  A cubic hesitant fuzzy set is a hybrid set which can express uncertain and hesitancy fuzzy information simultaneously. For multiple attribute decision-making problems in engineering practice, the complicated decision information is generally incomplete and indeterminate. Cubic hesitant fuzzy set can be a valuable tool for describing uncertain and hesitancy fuzzy information in uncertain decision environment. Nevertheless, no similarity measure has been used to solve decision-making problems under cubic hesitant environment in previous studies. This paper presents a Jaccard similarity measure between cubic hesitant fuzzy sets and investigates their properties. Then a multiple attribute decision-making method is developed based on the …weighted Jaccard similarity measure under cubic hesitant environment. Using this method, the similarity measure values between the ideal alternative and each evaluated alternative are determined to obtain the ranking order of similarity measure values and the best alternative. An illustrative example of the selection problem of project alternatives is utilized to illustrate the application of the developed decision-making method. Finally, the validity of the proposed decision-making method was demonstrated based on the comparison of the decision-making results of the illustrative example with two distance-based similarity measures. Show more

Keywords: Cubic hesitant fuzzy set, multiple attribute decision-making, similarity measure, Jaccard measure

DOI: 10.3233/JIFS-182555

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 1, pp. 1075-1083, 2019

Authors: Ulaganathan, M.S. | Devaraj, D.

Article Type: Research Article

Abstract: The Maximum Power Point Tracking (MPPT) controller plays a vital role in maximizing power output from the solar Photovoltaic (PV) sources. The tracking efficiency of the MPPT controller is affected by a rapidly varying environmental condition. This paper presents a novel MPPT controller for standalone PV system based on a Neural Network (NN) and Gain-scheduled Proportional Integral (GS-PI) controller to track the fast-changing Maximum Power Point (MPP).The NN model is trained to predict the operating parameters of the PV array at which maximum power is generated. The gain scheduled PI controller parameters are optimally tuned with Real-coded Genetic Algorithm (RGA) …to improve the controller performance. The developed MPPT controller is used to control the power converter in the solar PV system. The PV array along with the control scheme is developed using LabVIEW and Multisim environment. Further, the performance of the developed control strategy is experimentally validated with solar PV emulator and DC-DC boost converter under the varying irradiation conditions. The tracking performance of the developed MPPT controller is compared with the modified Perturb and Observe and NN+PI controller based MPPT controller. The experimental results reveal that the tracking performance of the developed MPPT technique is much improved and more accurate in MPP tracking. Show more

Keywords: Neural networks, Real Coded Genetic Algorithm, gain scheduled PI controller, P&O algorithm

DOI: 10.3233/JIFS-182556

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 1, pp. 1085-1098, 2019

Authors: Zeng, Wenyi | Li, Deqing | Yin, Qian

Article Type: Research Article

Abstract: Hesitant fuzzy linguistic term set(HFLTS), which permits decision makers to use several linguistic terms to assess a variable, is a useful tool to deal with situations in which people are hesitant in providing their assessment. In this paper, we introduce the concept of weighted hesitant fuzzy linguistic term set(WHFLTS), in which different weights are designed to these possible linguistic terms, and the weights indicate that the decision maker has different confidence in giving every possible assessment. After that, we introduce some operations such as union, intersection, complement, multiplication of weighted hesitant fuzzy linguistic elements, discuss their operation properties, and …propose the score function of the weighted hesitant fuzzy linguistic element(WHFLE) to compare weighted hesitant fuzzy linguistic elements(WHFLEs). Furthermore, we introduce the concept of hesitance degree of weighted hesitant fuzzy linguistic element, present four aggregation operators such as the weighted hesitant fuzzy linguistic weighted averaging(WHFLWA) operator, the weighted hesitant fuzzy linguistic weighted geometric(WHFLWG) operator, the generalized weighted hesitant fuzzy linguistic weighted averaging(GWHFLWA) operator and the generalized weighted hesitant fuzzy linguistic weighted geometric(GWHFLWG) operator to aggregate weighted hesitant fuzzy linguistic information, and build the mathematical model of multi-criteria group decision making based on weighted hesitant fuzzy linguistic environment. Finally, two numerical examples are used to illustrate the effectiveness and applicability of our proposed method. Show more

Keywords: Hesitant fuzzy sets, Hesitant fuzzy linguistic term sets, Weighted hesitant fuzzy linguistic term sets, Aggregation operator, Group decision making

DOI: 10.3233/JIFS-182558

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 1, pp. 1099-1112, 2019

Authors: Yi, Zeren | Li, Guojin | Chen, Shuang | Xie, Wei | Xu, Bugong

Article Type: Research Article

Abstract:  This paper presents a navigation algorithm based on interval type-2 fuzzy neural network fitting Q-learning (IT2FNN-Q), and succeeds in providing a solution for mobile robot navigation in complex environments. The algorithm utilizes the fuzzy reasoning adaptive ability and extensive functional approximation features of IT2FNN to solve this problem, mapped from state space to action space, of the Q-learning algorithm in unknown environments. Compared with the BP fitting Q-learning algorithm (BP-Q), IT2FNN-Q endows the robot with better adaptive and real-time decision-making abilities and solves the slow convergence and nonconvergence problems, through its local approximation. By comparison with the fuzzy neural network …fitting Q-learning algorithm (FNN-Q), this proposed algorithm has more advantages for dealing with the external uncertainty, enabling the robot to complete a better path with less fuzzy rules. The results of the simulation and comparison of the proposed method with FNN-Q and BP-Q revealed that the mobile robot can navigate itself in complex environments with fewer steps, obtaining more reward values by adopting the algorithm presented in this paper. Show more

Keywords: Mobile robots, Q-learning, robot navigation, interval type-2 fuzzy neural network (IT2FNN)

DOI: 10.3233/JIFS-182560

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 1, pp. 1113-1121, 2019

Authors: Xing, Yuping | Zhang, Runtong | Zhu, Xiaomin | Bai, Kaiyuan

Article Type: Research Article

Abstract: Multiple attribute decision making (MADM) problems widely exist in real decision making, and MADM methods with linguistic information have achieved great success. However, as the complexity of decision making problems is increasing in the real world, it is of great necessity to further develop new expression of evaluation information and aggregation technologies that can reflect the correlation among multi-attributes under uncertain decision-making environment. In response, this paper originally presents q -rung orthopair fuzzy uncertain linguistic set (q -ROULS) by combining q -rung orthopair fuzzy set (q -ROFS) and uncertain linguistic set (ULS). Then operational laws, expected functions and accuracy functions …of q -rung orthopair uncertain linguistic variables (q -ROULVs) are also defined. Considering the correlation between q -ROULVs, we propose a family of q -rung orthopair fuzzy uncertain linguistic Choquet integral operators to aggregate q -rung orthopair uncertain linguistic information. Further, a novel MADM technique is presented based on the proposed q -rung orthopair fuzzy uncertain linguistic Choquet integral operators. The developed MADM method with q -rung orthopair fuzzy uncertain linguistic information enriches fuzzy decision-making theory and provides a new way for decision makers (DMs) under q -rung orthopair fuzzy uncertain linguistic environment. Show more

Keywords: q-rung orthopair fuzzy uncertain linguistic set, q-rung orthopair uncertain linguistic choquet integral operators, multi-attribute decision making

DOI: 10.3233/JIFS-182581

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 1, pp. 1123-1139, 2019

Authors: Renisha, G. | Jayasree, T.

Article Type: Research Article

Abstract: The rapid development in technology has led to a colossal surge in the use of biometric authentication system. Speaker identification biometric is one of the fields that is under progress and demands more and more precision. The objective of this research is to explore the issue of identifying a speaker from voice regardless of the content. Perceptual Wavelet Packet Transform (PWPT) and Artificial Neural Networks (ANN) approach are discussed in this paper for speaker identification. Perceptual Wavelet Packet Cepstral Coefficients (PWPCC) are used for transforming speech into spectral feature vectors, and the most germane aspects of the speech signal are …selected from the energy and variance distribution characteristics. These selected attributes are presented to the Cascaded Feedforward Neural Network (CFNN) and trained with Levenberg-Marquardt Back Propagation (LMBP) algorithm for further classification. The performance of the network is determined by evaluating the Speaker Identification Rate (SIR). For comparison, five different gradient descent training algorithms are considered and it is found that the LMBP produces better performance. The proposed model is evaluated for clean as well as noisy speech at various SNR levels and is found to be competitive, and the experimental results show significant improvement in speaker identification rate compared with other classical methods. Show more

Keywords: Perception, wavelet, speaker, speech, neural network

DOI: 10.3233/JIFS-182599

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 1, pp. 1141-1153, 2019

Authors: Maini, Tarun | Kumar, Abhishek | Misra, Rakesh Kumar | Singh, Devender

Article Type: Research Article

Abstract:  This paper focuses on Fuzzy rough set, which is the fusion of fuzzy sets and rough sets theory for doing feature selection. For selecting the appropriate feature subset, swarm algorithms are used. The fitness function used here is Fuzzy Rough Dependency Measure. This paper demonstrates that by optimizing the fitness function, swarm algorithms are capable to select the best subset of features. Further, in this paper, an attempt has been made to improve the capability of the swarm based algorithms such as Intelligent Dynamic Swarm (IDS) and Particle Swarm Optimization (PSO) through modified initialization of solutions, for picking the appropriate …features for the feature selection task. Improvement in the size of reducts and classification accuracy of these reducts are observed when initialization is done using the proposed method. Statistical t-tests have also been performed for the validation of the results. Show more

Keywords: Feature selection, fuzzy rough set, rough set, particle swarm optimization, intelligent dynamic swarm, classification accuracy, t-test

DOI: 10.3233/JIFS-182606

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 1, pp. 1155-1164, 2019

Authors: Afrasiabi, Mousa | Afrasiabi, Shahabodin | Parang, Benyamin | Mohammadi, Mohammad

Article Type: Research Article

Abstract: Differential protection of power transformers, as the fundamental protection, plays an important role in power system reliability and security. The main challenge in differential protection is discrimination between internal faults of power transformers and inrush current. Development of differential protection, especially discrimination between internal faults from other disturbances, have been a favorite subject in power system protection field over decades. Traditional methods proposed so far have several shortcomings: i) high computational burden, ii) sensitivity to noise, iii) being influenced by predefined threshold value/additional parameters/different models at varying ambient conditions, and iv) dependence on handcrafted or spectral analysis to extract features. …Deep neural networks (DNN) is selected as the potential solution in this paper, which is able to capture the hierarchical features of a half-cycle of raw data. This paper proposes convolutional neural networks (CNN), in which batch normalization and scaled exponential linear unit (SELU) are merged to enhance differential protection performance. In order to generalize the CNN-based differential protection, several external factors, i.e. the compensation error of current transformer (CT) saturation, series compensated line, and superconducting fault current limiter (SFCL) are conducted to verify the reliability of the proposed method through different reliability metrics. The simulation and experimental results are assessed to show high reliability and the speed of the proposed method. Show more

Keywords: Inrush current, Power transformer protection, Differential protection, Convolutional neural network (CNN)

DOI: 10.3233/JIFS-182615

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 1, pp. 1165-1179, 2019