Journal of Intelligent & Fuzzy Systems - Volume 35, issue 5

Authors: Xue, Zhan-Ao | Xin, Xian-Wei | Yuan, Yi-Lin | Xue, Tian-Yu

Article Type: Research Article

Abstract: Over the last decade, a lot of attentions have been drawn in the problem of multiple attribute decision-making associated with incomplete information tables. The filling missing value method and minimum decision cost are two important challenges for incomplete information tables. But most published studies focus on the optimization of data reckoning without considering the risk appetite of decision makers and decision-making environments. In this paper, considering the above factors, we presented an attribute weight determination method and an effective computing method for dealing with intuitionistic fuzzy incomplete information tables. Then, delay-refused decision in the boundary region and the risk assessment …based on cross-entropy were proposed. Finally, a secondary decision strategy based on the probability entropy of delay-accepted decision and the application algorithms were given for improving the quality of decisions. Show more

Keywords: Intuitionistic fuzzy possibility measure, incomplete information table, three-way decisions, secondary decision, cross-entropy

DOI: 10.3233/JIFS-171725

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 5, pp. 5657-5666, 2018

Authors: Chen, Yumin | Zhuang, Ying | Zhu, Shunzhi | Li, Wei | Tang, Chaohui

Article Type: Research Article

Abstract: The theory of rough sets is an efficient mathematical tool for dealing and reasoning with uncertainty information systems. The measures of traditional rough sets are applicable to discrete-valued information systems, but not suitable to real-valued data sets. In this paper, by introducing a distance matrix to granulate these real-valued data, a granulated fuzzy rough set model is proposed, which combines fuzziness and roughness into a rough set theoretical framework. By constructing a fuzzy similar relation with a distance matrix form, real-valued data sets can be deal with. We also define some operations on the fuzzy relations and fuzzy granules. Furthermore, …two kinds of measures of fuzzy granules are proposed, which are information entropy measure and information granularity measure. These measures are calculated by a novel representation with a fuzzy granule matrix. As a result, uniform representations of fuzzy rough sets and their information measures are formed in this work. Show more

Keywords: Granular computing, fuzzy rough sets, uncertainty measures, information entropy, information granularity

DOI: 10.3233/JIFS-171946

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 5, pp. 5667-5677, 2018

Authors: Wu, Xiu-Yun | Bai, Shi-Zhong

Article Type: Research Article

Abstract: In this paper, we investigate the multiple attribute decision making problems with Pythagorean fuzzy information. Then, we first introduce some operations on Pythagorean fuzzy sets, and further develop the induced Pythagorean fuzzy ordered weighted geometric (IPFOWG) operator. We also establish some desirable properties of this operator, such as commutativity, idempotency and monotonicity. Then, we apply the induced Pythagorean fuzzy ordered weighted geometric (IPFOWG) operator to solve the Pythagorean fuzzy multiple attribute decision making problems. Finally, an illustrative example for evaluating the operation performance of high-tech zone technology business incubator network is given to verify the developed approach and to demonstrate …its practicality and effectiveness. Show more

Keywords: Multiple attribute decision making, Pythagorean fuzzy numbers, induced Pythagorean fuzzy ordered weighted geometric (IPFOWG) operator, operation performance, high-tech zone technology business incubator network

DOI: 10.3233/JIFS-172018

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 5, pp. 5679-5687, 2018

Authors: Wang, Sichun | Xia, Fei

Article Type: Research Article

Abstract: A knowledge base is one basic notion in rough set theory. In a knowledge base, one can approximately describe target notions by using existing knowledge structures. This paper investigates invariant characteristics of knowledge structures in a knowledge base under homomorphisms and their uncertainty measures. First, partial dependence knowledge structures in the same knowledge base is proposed and the concept of knowledge structure bases is presented. Then, invariant and inverse invariant characteristics of knowledge structures in a knowledge base under homomorphisms are obtained. Next, measuring uncertainty of knowledge structures in the same knowledge base is investigated. Finally, two examples are employed …to illustrate that knowledge granulation, rough entropy, knowledge entropy and knowledge amount of knowledge structures, and knowledge distance between knowledge structures are neither invariant nor inverse invariant under homomorphisms, and third example shows the feasibility of the proposed measures for uncertainty of knowledge structures in the same knowledge base. These results will be helpful for building a skeleton of granular computing in knowledge bases. Show more

Keywords: Rough set theory, knowledge base, knowledge granule, knowledge structure, dependence, homomorphism, characteristic, uncertainty

DOI: 10.3233/JIFS-172048

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 5, pp. 5689-5705, 2018

Authors: Hayat, Khizar | Ali, Muhammad Irfan | Alcantud, José Carlos R. | Cao, Bing-Yuan | Tariq, Kalim U.

Article Type: Research Article

Abstract: Concept selection is among the most crucial activities in new product development, since the consequences of a bad choice may be disastrous. The recent research of concept selection benefited from soft set theory as an effective tool for that purpose. However, some uncertainties still affix in design concept evaluation, which can be overcome by utilizing membership and non-membership functions on design attributes. In this paper, a novel approach of design concept evaluation is introduced based on generalized intuitionistic fuzzy soft sets. By combining bijective soft set and intuitionistic fuzzy set, design concepts are specified on design attributes. Generalized b -intuitionistic …fuzzy soft sets are used to illustrate the mapping between customer demands and generated design concepts. An algorithm is proposed to select an appropriate object from a set of objects for one or more than one customers. Finally, we present a comparison of our methodology with focal existing methods, and we identify some of their limitations. Show more

Keywords: Decision-making, new product development, concept evaluation, generalized intuitionistic fuzzy soft sets, bijective soft sets

DOI: 10.3233/JIFS-172121

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 5, pp. 5707-5720, 2018

Authors: Khalil, Ahmed Mostafa | Alkhazaleh, Shawkat | Li, Sheng-Gang | You, Fei | Ma, Sheng-Quan

Article Type: Research Article

Abstract: The aim of this paper is to correct the assertions (3) and (4) of Proposition 5.2 proposed by Alkhazaleh [Journal of Intelligent and Fuzzy Systems 30 (2016) 1087-1098]. Every notions involved are extend to the arbitrary set case in a clear, rigorous, and non-burdensome manner. A counterexample illustrates the flaw of the assertions. We introduce some new notions describing ‘subset’ of time-neutrosophic soft sets (T-NSSs for short) and ‘equal’ of T-NSSs, and give some examples and related propositions. Then we use these results to remedy the flaw of the assertions, and to improve the work of Alkhazaleh.

Keywords: Neutrosophic set, soft set, neutrosophic soft set, time-neutrosophic soft set

DOI: 10.3233/JIFS-172203

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 5, pp. 5721-5728, 2018

Authors: Hazarika, Bipan

Article Type: Research Article

Abstract: We introduce the notions of pointwise and uniform ideal convergence and kind of convergence lying between aforementioned convergence methods, namely, equi-ideal convergence of sequences of fuzzy valued functions and obtain various results related to these kinds of convergence and their representations of sequences of α -level cuts. We prove the ideal version of the Egorov’s theorem for sequences of fuzzy valued measurable functions defined on a finite measure space ( X , M , μ ) . We also define the notion of ideal convergence in measure for sequences of fuzzy valued functions and prove …some interesting results. Show more

Keywords: Fuzzy valued function, ideal convergence, Egorov’s theorem, convergence in measure

DOI: 10.3233/JIFS-17237

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 5, pp. 5729-5740, 2018

Authors: Jiang, Rong | Yang, Zhi-Xia

Article Type: Research Article

Abstract: In this paper, a novel learning frameworks–multiple rank multi-linear twin support matrix classification machine (MRMLTSMCM) is outlined, as an extension of twin support vector machine (TWSVM). Different from TWSVM, MRMLTSMCM uses two pairs of projecting matrixes to construct the pair of functions, which are used to establish decision function. Compared with the vector-based method, the matrix-based could not only keep the structure of the matrix data but also reduce computational complexity. In addition, a regularization term is considered adding to improve the performance of MRMLTSMCM. Moreover, a novel algorithm for MRMLTSMCM is introduced. Finally, experimental results show the effectiveness of …the method by classification accuracy, convergence behavior and computation time. Show more

Keywords: Classification, matrix learning, tensor learning

DOI: 10.3233/JIFS-17414

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 5, pp. 5741-5754, 2018

Authors: Zheng, Wei | Wang, Hongbin | Wen, Shuhuan | Wang, Hongrui | Zhang, Zhiming

Article Type: Research Article

Abstract: This paper addresses the T-S fuzzy robust dynamic output-feedback control problem for a class of nonlinear continuous-time systems with parametric uncertainties and premise variables. First, based on the control input matrix and output matrix, the parametric uncertainties are assumed to be a subsystem, which is described as a linear fractional. Secondly, the nonlinear continuous-time systems are described by the Takagi–Sugeno (T–S) fuzzy model. Then the new dynamic output feedback controller is designed based on the T–S fuzzy model and the linear fractional (parametric uncertainties), and the sufficient conditions for robust stabilization are given in the form of linear matrix inequalities …(LMIs). Compared with previous work, the developed methods not only have abilities to handle the fuzzy system with premise variables but also can deal with the parametric uncertainties effectively. The results are further extended to a mobile robot case and a chemical process case. Finally, numerical examples are performed to show the effectiveness of the theoretical results. Show more

Keywords: Dynamic output-feedback, T-S fuzzy model, parametric uncertainties, premise variables, linear fractional, linear matrix inequalities

DOI: 10.3233/JIFS-17934

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 5, pp. 5755-5769, 2018

Authors: Duan, Lixiang | Xie, Mengyun | Wang, Jinjiang | Bai, Tangbo

Article Type: Research Article

Abstract: With movement toward complication and automation, modern machinery equipment encounters the problems of diversity and complex origination of faults, incipient weak faults, complicated monitoring systems, and massive monitoring data, which are all challenging current fault diagnosis technologies. Conventional machine learning techniques, such as support vector machine and back propagation, have disadvantages in handling the non-linear relationships and complicated structure of massive data. Deep learning (DL) methods have a greater capability to address complex and heterogeneous machinery signals, and identify faults more accurately. This paper presents a review of DL methods in emerging research in the machinery fault diagnosis field. First, …common DL models are briefly described. Then, the application of DL to machinery fault diagnosis is described in detail, including the problems DL aims to solve and the achievements it has accomplished thus far. To demonstrate the capability of DL to handle the multiplicity and complexity of equipment faults and massive data, we examine experimental results for typical reciprocating compressor and bearing. Finally, the limitations and trends of further DL development are discussed. Show more

Keywords: Deep learning, machinery fault diagnosis, feature learning, conventional machine learning

DOI: 10.3233/JIFS-17938

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 5, pp. 5771-5784, 2018