Journal of Intelligent & Fuzzy Systems - Volume 39, issue 1

Authors: Wang, Jun | Tang, Fangcheng | Shang, Xiaopu | Xu, Yuan | Bai, Kaiyuan | Yan, Yusheng

Article Type: Research Article

Abstract: The recently proposed q -rung orthopair fuzzy sets (q -ROFSs) have been proved to be an effective tool to describe decision makers’ evaluation information and this paper attempts to propose a new multi-attribute group decision-making (MAGDM) method with q -rung orthopair fuzzy information. First of all, we propose a new score function of q -rung orthopair fuzzy numbers (q -ROFNs) by taking the hesitancy degree into account. When considering to fuse q -ROFNs, this paper tries to propose some novel aggregation operators. The power geometric (PG) operator has the ability of reducing or eliminating the bad influence of decision makers’ …unreasonable assessments on final decision results. Hence, we extend PG to q -ROFSs and propose the q -ROF power geometric operator and its weighted form. The most prominent advantage of dual Muirhead mean (DMM) is that it can capture the interrelationships among any numbers of input arguments. To take full advantages of PG and DMM, we further combine PG with DMM within q -rung orthopair fuzzy environment and propose the q -rung orthopair fuzzy power dual Muirhead mean, and q -rung orthopair fuzzy weighted power dual Muirhead mean operators. The proposed operators can reduce the negative effects of unreasonable evaluations on the decision results, and simultaneously take the interrelationship among any numbers of input arguments into account. In addition, we propose a new MAGDM method based on the proposed aggregation operators. Finally, we provide numerical examples to demonstrate the validity and merits of the proposed method. Show more

Keywords: q-rung orthopair fuzzy set, power geometric operator, dual muirhead mean, q-rung orthopair fuzzy power dual muirhead mean, novel score function, multi-attribute group decision-making

DOI: 10.3233/JIFS-191552

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 561-580, 2020

Authors: Li, Hongxu | Yang, Yang | Yin, Songyi

Article Type: Research Article

Abstract: The q -rung orthopair fuzzy set is a significant part of the existing orthopair fuzzy sets, whose advantage is to more comprehensively describe uncertain information. For q -rung orthopair fuzzy sets, the correlation between them is generally measured by the correlation coefficient. In order to express the positive and negative correlations of q -rung orthopair fuzzy sets simultaneously from a statistical perspective, and to reflect the attitude of decision makers, in this paper, two new correlation coefficients of q -rung orthopair fuzzy sets are proposed and investigated. Firstly, a λ -variance-based correlation coefficient of q -rung orthopair fuzzy sets is …proposed from the statistical viewpoint. Secondly, a λ -matching-function-based correlation coefficient of q -rung orthopair fuzzy sets is defined from the perspective of vector calculation. In the end, an example of clustering analysis is presented to verify the feasibility and superiority of the proposed correlation coefficients by comparing with other existing correlation coefficient of q -rung orthopair fuzzy sets. It can be seen from the clustering results that the two new λ -correlation coefficients not only consider the positive or negative correlation at the same time, but also can be dynamically adjusted according to the needs of decision makers. Furthermore, clustering results using λ -variance-based and λ -matching-function-based correlation coefficients converge faster than clustering results using the existing correlation coefficient in the q -rung orthopair fuzzy environment. Show more

Keywords: q-rung orthopair fuzzy set, correlation coefficient, clustering analysis

DOI: 10.3233/JIFS-191553

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 581-591, 2020

Authors: Li, Guanrong | Qiu, Jianjun | Yang, Xiaopeng

Article Type: Research Article

Abstract:  Considering the application in wireless communication basic-station (terminal) system, we investigate the weighted minimax programming subject to two-sides fuzzy relation inequalities with max-product composition in this paper. By establishing the maximum solution and the discrimination matrix of the inequalities system, we give the sufficient and necessary condition that the inequalities system is consistent and further obtain the structure of the solution set. We develop a solution matrix approach method for solving the proposed problem and further develop a step-by-step algorithm for carrying out the method. The theory analysis and numerical example indicate that the algorithm is feasible and efficient.

Keywords: Two-sides fuzzy relation inequalities, max-product composition, weighted minimax programming, solution matrix approach, nonlinear optimization

DOI: 10.3233/JIFS-191565

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 593-605, 2020

Authors: Zhou, Yafu | Wang, Hantao | Li, Linhui | Lian, Jing

Article Type: Research Article

Abstract: The efficiency and control accuracy of Interior Permanent Magnet Synchronous Motor (IPMSM) are the main factors affecting performance. Manual calibration has the disadvantage of high work intensity, long calibration period and high technical requirement, which leads to low calibration accuracy and motor efficiency. Thus, a novel calibration method based on Deep Deterministic Policy Gradient (DDPG) and Long Short-Term Memory (LSTM) is proposed. By constructing a deep reinforcement learning network, the self-optimization of the optimal working point under any working condition is realized, and the MAP for IPMSM in full speed-torque range is obtained. The method can be used to quickly …realize the optimal matching of d-q axis current with arbitrary stator current. It focuses on solving the problem of motor overheating caused by long adjustment time of manually calibrated MAP when the motor is overloaded, to realize fast calibration in overload area. Moreover, the method reduces the dependence on the motor parameters and increases the adaptability of the calibration MAP data to the operating conditions. The simulation and bench test indicate that the method can meet the response requirements of motor torque, and results reveal that the motor efficiency is greatly improved. Show more

Keywords: Interior permanent magnet synchronous motor, deep reinforcement learning, bench calibration, optimal control, optimal efficiency

DOI: 10.3233/JIFS-191567

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 607-626, 2020

Authors: Liu, Peide | Zhang, Pei

Article Type: Research Article

Abstract: A normal wiggly hesitant fuzzy set (NWHFS) is a powerful and useful tool to dig the potential indeterminacy of decision makers (DMs) in the process of expressing their preferences, which can be considered as an extended form of the traditional hesitant fuzzy set (HFS). The NWHFSs can not only retain the original hesitant fuzzy information completely, but also explore potential uncertainty of theses information. TODIM is an effective method to capture the psychological behavior based on prospect theory. Considering the advantages of NWHFS and TODIM method, in this paper, we define the distance measure of any two normal wiggly hesitant …fuzzy elements (NWHFEs), and put forward an extended normal wiggly hesitant fuzzy TODIM (NWHF-TODIM) approach to handle multiple attribute decision making (MADM) problems with normal wiggly hesitant fuzzy (NWHF) information. Then we use the extended NWHF-TODIM method to rank alternatives and select an ideal one. Lastly, we compare it with two existing approaches to verify the rationality and validity of the proposed approach. Show more

Keywords: Normal wiggly hesitant fuzzy sets, multiple attribute decision making, TODIM

DOI: 10.3233/JIFS-191569

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 627-644, 2020

Authors: Kesicioğlu, M. Nesibe | Şamlı, Esra

Article Type: Research Article

Abstract: In this paper, orders based on uni-nullnorms on bounded lattices are introduced and discussed. By this way, the existing orders in the literature induced by t-norms, t-conorms, uninorms and nullnorms are extended to much more general form. The relationships between the orders induced by uni-nullnorms and the orders induced by their underlying t-norms, t-conorms, uninorms and nullnorms are presented. A necessary and sufficient condition making a bounded lattice again a lattice with respect to the orders based on uni-nullnorms is given. Also, the relationships between the partially ordered sets based on the orders induced by t-norms and induced by their …N-dual t-conorms and conjugate t-norms, which are special uni-nullnorms, are investigated. Show more

Keywords: Uninorm, bounded lattice, partial order, uni-nullnorm

DOI: 10.3233/JIFS-191583

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 645-663, 2020

Authors: Pundhir, Sandhya | Ghose, Udayan | Bisht, Upasana

Article Type: Research Article

Abstract: One of the momentous transformation performed by an artificial neural network (ANN), Support Vector Machine (SVM), Radial basis Function (RBF) and many other machine learning method is the application of activation function. MyAct the proposed activation method is used here with various ANN architectures for link prediction, classification and general prediction. Statistical properties of data used here to prove the effectiveness of proposed activation function MyAct over other popular activation methods. A data dependent transfer method is developed, which is pioneer in its own way. This proves to be an unified formulation for the robust and generalised learning for the …classification, link prediction and regression problem types. Classification is done with Iris dataset using ANN with different activation method and results are compared. Improved results are achieved when MyAct used with Tailored Deep Feed Forward Artificial Neural Network (TDFFANN), simple Artificial Neural Network and Deep Artificial Neural Network. Aim here is to develop a novel activation method which work with positive data, negative data, small size data, big size data, skewed data or corrupt data. An attempt is made to cover complete versatile behaviour of data. Currently not a single activation method can work well on all above mentioned data. Results obtained using MyAct on the datasets used here proves it to be a good choice in comparison to logsig, tansig and other popular activation methods for classification and link prediction. Satisfactory improvement is achieved by using data length as well as negative range values in the prediction done by proposed method. MyAct had 22% better standard deviation than ReLU (Rectified Linear unit) and 36. 28% better standard deviation than ELU (Exponential linear unit). MyAct has 2. 6% better accuracy in regression error than Swiss method and 2. 5% better accuracy in regression error than ELU. Other results are discussed in the paper. Show more

Keywords: Artificial neural network, activation function, feedforward neural network, deep learning, ink prediction, machine learning

DOI: 10.3233/JIFS-191618

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 665-677, 2020

Authors: Rao, Xiansheng | Liu, Keyu | Song, Jingjing | Yang, Xibei | Qian, Yuhua

Article Type: Research Article

Abstract:  Presently, the Gaussian kernel approach has been widely accepted for measuring the similarities among samples and then constructing various fuzzy rough sets. Notably, the considered parameter plays a crucial role in deriving Gaussian kernel based similarities. This is mainly because different parameters will generate different scales of the similarities. From this point of view, different parameters may result in different fuzzy rough approximations and the corresponding reducts. Generally speaking, to search a parameterized reduct with better generalization performance, a naive approach can be designed by repeating the process of computing reduct through using different parameters. Obviously, it is very time-consuming. …To fill such a gap, an acceleration approach is proposed which aims to reduce the elapsed time of searching reducts based on different parameters. The main mechanism of our proposed approach is to take the variation of the used parameters into account, and then the process of finding reduct under current parameter can be realized based on the previous parameter related reduct. The experimental results over 16 UCI data sets, which are obtained by testing different Gaussian kernel based fuzzy rough sets, demonstrate that our proposed acceleration strategy not only can significantly reduce the time consumption of finding reducts in terms of different parameters, but also will not lead to poorer classification performance and significant variation of length of the obtained reducts by comparing with the results obtained by the naive process. This study suggests technical support for quickly finding reducts of parameterized fuzzy rough sets. Show more

Keywords: Acceleration strategy, attribute reduction, fuzzy dependency, fuzzy rough set, Gaussian kernel

DOI: 10.3233/JIFS-191633

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 679-695, 2020

Authors: Mahajan, Rutal | Zaveri, Mukesh

Article Type: Research Article

Abstract: Human beings often use figurative language during communication to express their thoughts. Uncovering the meaning out of figurative language is not as simple as literal language. Humor identification is considered to be an important linguistic device for sentiment analysis of figurative text because it can often change the sentiments of the text. Moreover, during verbal communication people use facial expressions, gestures and other modalities to convey their feeling and to automatically understand the meaning out of figurative sentences using these modalities is part of computer vision and digital image processing. It is difficult for written sentences where facial expressions, gestures, …other modalities, and emotions are absent and so it is an interesting question of research. Humor is a figurative device and a creative linguistic phenomenon. To understand the meaning of humor, we need to correctly understand the mood and emotions conveyed in the text, which is beyond the semantics of literal language communication. In this work, we have addressed these issues of understanding the emotions using affect-based information from text with various well established machine learning classifiers. We have exploited various affective content that inhibits the emotions and feeling of a writer such as emoticons, writing styles like punctuation, capitalization, sentiment words and so on. The proposed affect-based humor identification model is evaluated on the SemEval 2017 HashTagWars dataset and yelp review dataset with different types of the experimental configuration. This evaluates the effectiveness of the proposed humor identification model with different types of features. Show more

Keywords: Humor identification, affective computing, natural language processing, machine learning

DOI: 10.3233/JIFS-191648

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 697-708, 2020

Authors: Alzubi, Maen | Kovacs, Szilveszter

Article Type: Research Article

Abstract: Fuzzy Rule Interpolation (FRI) is an important technique for implementing inference with sparse fuzzy rule-bases. Even if a given observation has no overlap with the antecedent of any rule from the rule-base, FRI may still conclude a conclusion. This paper introduces a new method called “Incircle FRI” for fuzzy interpolation which is based on the incircle of a triangular fuzzy number. The suggested method is defined for triangular CNF fuzzy sets, for a single antecedent universe and two surrounding rules from the rule-base. The paper also extends the suggested “Incircle FRI” to trapezoidal, and hexagonal shaped fuzzy sets by decomposing …their shapes to multiple triangulars. The generated conclusion is also a CNF fuzzy set. The performance of the suggested method is evaluated based on numerical examples and a comprehensive comparison to other current FRI methods. Show more

Keywords: Fuzzy interpolative reasoning, sparse fuzzy rule-based systems, incircle triangular fuzzy numbers, incircle FRI method

DOI: 10.3233/JIFS-191660

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 709-729, 2020