Journal of Intelligent & Fuzzy Systems - Volume 31, issue 6

Authors: Sun, Shilei | Ionita, Silviu | Volná, Eva | Gavrilov, Andrey | Liu, Feng

Article Type: Editorial

Abstract: Newsday fuzzy systems are applied in many interdisciplinary fields, in which artificial intelligent tools are utilized to analyze different data in the corresponding fields. Under this background, the 2nd International Conference on Fuzzy Systems and Data Mining (FSDM 2016), organized by Macau University of Science and Technology, is scheduled to be held in Macau, December 11–14, 2016. This special issue contains some selected papers related to fuzzy systems, data mining and their applications, which provides some highlights on fuzzy systems and data mining during the past years in East Asia.

Keywords: Fuzzy logic, artificial intelligence system, data mining

DOI: 10.3233/JIFS-169156

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 6, pp. 2757-2758, 2016

Authors: Li, Ting | Zhang, Yue | Du, Fang

Article Type: Research Article

Abstract: This paper discusses an international portfolio selection problem under fuzzy environment. Using the possibilistic theory, we first propose an general international portfolio selection model with exchange rate risk under the assumption that the values of unit investment in risky assets and exchange rates are fuzzy numbers. With this model, the investors can not only consider the foreign investment risk but also the domestic investment risk, which give the investors more selections in facing various risks. Furthermore, we deduce an equivalent model, when investment values of risk assets and exchange rates are triangular fuzzy numbers. Then, an numerical study is carried …out with a portfolio of six assets denominated in the local currency. Based on the data, we obtain the portfolio frontier with exchange rate risk, and compare it with the portfolio frontier without domestic asset. The results illustrate the effectiveness of the proposed model. Show more

Keywords: Exchange rate risk, fuzzy number, possibilistic theory, portfolio, efficient frontier

DOI: 10.3233/JIFS-169157

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 6, pp. 2759-2765, 2016

Authors: Zhang, Xiaoyan | Wei, Ling | Luo, Shuqun | Xu, Weihua

Article Type: Research Article

Abstract: Recently, making decisions and analyzing data are getting more and more attention by taking advantage of rough set and intuitionistic fuzzy set theories. Additionally, it can be found that many works have been developed about intuitionistic fuzzy rough set approaches from different viewpoints. In this article, we introduce similarity degrees and four kinds of uncertainty measurement, called θ -conditional entropies, θ -similarity intuitionistic fuzzy accuracies, θ -similarity intuitionistic fuzzy roughness and θ -rough decision entropies in intuitionistic fuzzy decision tables. Also, we provide a novel method for classifying the objects’ intuitionistic fuzzy decision table. Moreover, we carefully discuss the lower …approximation and upper approximation of a given set and classify their important properties based on the novel classes in the intuitionistic fuzzy decision table. Furthermore, an illustrated example is employed to demonstrate the conceptual arguments of these measurements based on different similarity degrees and similarity rates. From this, it can be found that the new measures are superior to the classical accuracy and roughness, and the method is valuable and useful in real life situations. Show more

Keywords: Classifications, decision information table, intuitionistic fuzzy set, rough set, similarity measures

DOI: 10.3233/JIFS-169158

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 6, pp. 2767-2777, 2016

Authors: Yang, Wei | Shi, Jiarong | Zheng, Xiuyun | Pang, Yongfeng

Article Type: Research Article

Abstract: Hesitant interval-valued intuitionistic fuzzy linguistic set has been developed by extending hesitant fuzzy set, interval-valued intuitionistic fuzzy values and linguistic terms. Some operational laws and distance measures for hesitant interval-valued intuitionistic fuzzy linguistic elements are defined. Several new generalized aggregation operators are proposed for hesitant interval-valued intuitionistic fuzzy linguistic information and some special cases of new operators are studied. Two new multiple attribute decision-making methods have been proposed using new aggregation operators and TOPSIS method. Painting selection problem has been presented to illustrate new methods.

Keywords: Multiple attribute decision making, hesitant interval-valued intuitionistic fuzzy linguistic set, hesitant linguistic decision making

DOI: 10.3233/JIFS-169159

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 6, pp. 2779-2788, 2016

Authors: Yan, Chun | Sun, Haitang | Liu, Wei

Article Type: Research Article

Abstract: Property insurance companies in China have accumulated certain customer resources, and these resources generate greater competitive challenges. In view of this, it is highly significant to the development of these companies to deeply analyze the individual demands of existing customers and to develop a broader cross-selling business based on the effective means of data mining tools. In this paper, the fuzzy c-means algorithm is introduced to association rules mining. Additionally, the improved Apriori algorithm-Fuzzy Association Rules Mending Apriori Algorithm based on fuzzy c-means is presented. The time complexity and space complexity of the proposed algorithm is reduced, and the application …scope is expanded to uncertain environment. Furthermore, an example is given to illustrate the use of the proposed methods. With the help of data mining tools, six main valuable fuzzy association rules are mined, and one cross-selling model is built based on property insurance customers’ data sets. Show more

Keywords: Cross-selling, data mining, customer maintenance, fuzzy association rules mending apriori algorithm

DOI: 10.3233/JIFS-169160

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 6, pp. 2789-2794, 2016

Authors: Zhao, Aiwu | Guan, Shuang | Guan, Hongjun

Article Type: Research Article

Abstract: Fuzzy time series forecasting models have been widely used for predictions in various domains, including the prediction of stock prices, project costs, academic enrollment, electric load demand, etc. Current studies in this field mainly focus on three issues: the discretization of real numbers, the expression of evolutionary rules generated from training data and the defuzzification of the forecasted fuzzy results. To automatically and intelligently determine the discretization intervals, this paper introduces a general entropy measuring (GEM) method into the partitioning process of the original time series. A computational algorithm is also designed to realize the auto-determination process for each subset. …Then, an improved hierarchical architecture is employed to express the fuzzy logical evolutionary rules of the fuzzy time series. To compare the performance of the proposed model with that of other models, the commonly used Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) dataset is used as experimental data. The forecasting results are evaluated using the root mean squared error (RMSE). The comparison results demonstrate the superiority of the proposed model. Finally, the Shanghai Stock Exchange Composite Index (SHSECI) stock price datasets from 1991 to 2014 are collected and used to test the model’s applicability. The empirical results show that the proposed model can effectively handle large online datasets. Show more

Keywords: Fuzzy time series, GEM-based discretization, evolutionary rules, fuzzy logical relation, forecasting model

DOI: 10.3233/JIFS-169161

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 6, pp. 2795-2806, 2016

Authors: Zhang, Yong | Zhang, Jianhua | Guo, Yinan | Sun, Xiaoyan

Article Type: Research Article

Abstract: Cost-based feature selection is an important data preprocessing technique in classification problems. This paper focuses on a real case that the cost that may be associated with features is fuzzy number. First, a fuzzy transforming method is introduced to transform fuzzy cost-based feature selection problems into ones with interval number. Second, an effective feature selection algorithm based on interval multi-objective particle swarm optimization is proposed. In this algorithm, a risk coefficient that decision makers are willing to bear when delete any solution is used to update the archive. Also, an interval crowding distance measure is adopted to evaluate the distribution …of non-dominated particles. Finally, feasibility of the presented algorithm is validated by simulation results. The results show that our algorithm is capable of generating excellent approximation of the true Pareto front. Show more

Keywords: Feature selection, fuzzy, interval, multi-objective, particle swarm optimization

DOI: 10.3233/JIFS-169162

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 6, pp. 2807-2812, 2016

Authors: Fang, Ruiming | Wu, Mingling | Jiang, Shunhui

Article Type: Research Article

Abstract: To satisfy the requirements of on-line status assessment of wind turbines, a fuzzy comprehensive evaluation method combined with normal cloud model based on SCADA data was proposed. Firstly, a concept was introduced, The Dynamic Inferior, which can take both the variation of assessment indices into the calculation process of inferior degree and the changing of the wind turbine operating condition into consideration. A Markov chain model was adapted to predict the variation of assessment indices. Then, a normal cloud model with the dynamic inferior degree as inputs was adopted to calculate the membership degree of different assessment indices to overcome …the subjectivity of normal membership functions. Furthermore, a fuzzy comprehensive assessment method was developed to assess the status of wind turbines online. Finally, the method was tested on the historical SCADA data of wind turbines. The results showed that this method was capable of not only accurately assessing the status of wind turbines online, but could also warn of problems early, which can prevent the occurrence of serious complications. Show more

Keywords: On-line status assessment, wind turbine, normal cloud model, dynamic inferior degree, fuzzy comprehensive evaluation method

DOI: 10.3233/JIFS-169163

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 6, pp. 2813-2819, 2016

Authors: Chen, Shuangshuang | Li, Bing | Li, Baochen | Dong, Jun

Article Type: Research Article

Abstract: It has been proven that the dendritic lattice neural network (DLNN) has the advantages of fast calculation, nonexistent convergence problems, and a superior capacity to store information. However, several datasets have also shown that the DLNN still suffers from low classification accuracy problems. This paper proposes that the main reason behind this problem is that the original DLNN cannot classify the samples that fall outside of all the hyperboxes. In order to solve this problem, a fuzzy inclusion measure is introduced to improve DLNN model’s testing algorithm. The improved testing algorithm of the DLNN model consists of two parts: (1) …the classification of samples covered by a hyperbox with the DLNN model, and (2) the classification of samples outside all of the hyperboxes based on the principle of maximum membership degree. Throughout this study, four standard datasets were employed to evaluate the effectiveness of the improved DLNN (based on comparisons with the original DLNN). Experimental results show that, in both the training and testing samples, the improved DLNN is capable of higher classification accuracies than the original DLNN. Show more

Keywords: Dendritic lattice neural network, fuzzy inclusion measure, hyperbox, maximum membership degree

DOI: 10.3233/JIFS-169164

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 6, pp. 2821-2827, 2016

Authors: Zhang, Chao | Zhai, Yanhui | Li, Deyu | Mu, Yimin

Article Type: Research Article

Abstract: Steam turbine fault diagnosis is a significant issue in fault diagnosis technology, which has made remarkable progress in the area of electromechanical engineering. Although many studies based on fuzzy approaches are developed on this topic, they can only cope with incomplete and uncertain information, but have limitations in processing indeterminate and inconsistent information in practical decision-making procedures. In addition, it is beneficial to solve problems under group decision-making background that aims to aggregate each expert’s preference to reach a final conclusion by consensus and unanimity. To deal with these difficulties in steam turbine fault diagnosis, by combining multigranulation rough sets …over two universes with single-valued neutrosophic sets theories, a single-valued neutrosophic multigranulation rough set over two universes is investigated in this paper. Then, we construct a general decision-making rule through using single-valued neutrosophic multigranulation rough sets over two universes within the background of steam turbine fault diagnosis. Finally, the validity of the decision-making method is verified by an illustrative case. Show more

Keywords: Steam turbine fault diagnosis, single-valued neutrosophic sets, multigranulation rough sets over two universes, group decision-making

DOI: 10.3233/JIFS-169165

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 6, pp. 2829-2837, 2016