Journal of Intelligent & Fuzzy Systems - Volume 33, issue 3

Authors: Xu, Changlin

Article Type: Research Article

Abstract: In this paper novel measuring distances (Minkowski distances) between intuitionistic fuzzy sets (IFSs) are given by a detailed analysis of the distance measures for IFSs proposed in the past. In the new method, the membership degree and non-membership degree are introduced into the distances between IFSs, while the assignments of the hesitancy degree to membership degree and non-membership degree are also considered, which is consistent with human cognition. The advantage of the novel distance measures are compared in depth by artificial intuitionistic fuzzy sets presented in literature. Finally, we demonstrate the efficiency of the proposed distance measures based on the …pattern recognition problems and medical diagnosis. Show more

Keywords: Intuitionistic fuzzy sets, distance measure, similarity measure, pattern recognition, medical diagnosis

DOI: 10.3233/JIFS-17276

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 3, pp. 1563-1575, 2017

Authors: Hu, Ziyu | Yang, Jingming | Cui, Huihui | Sun, Hao | Wei, Lixin

Article Type: Research Article

Abstract: Convergence and well-distribution are two basic issues in multi-objective optimization problems(MOPs). However, it is hard to optimize them simultaneously for the selection of leader particle is not always leading the population to the Pareto front. To make a better performance of multi-objective particle swarm optimization algorithm(MOPSO), decomposition and domination leadership particle selection mechanism have been introduced into MOPSO. Decomposition leader particle selection mechanism is used to keep the swarm with diversity, while domination leader particle selection mechanism make the particles move to the Pareto front. The performance of our proposed method is validated based inverted generation distance(IGD) and compared with …five state-of-the-art algorithms on a number of unconstrained benchmark problems. Empirical analysis demonstrates the superiority of our proposed method on both proximity and diversity. Show more

Keywords: Intelligence algorithm, multi-objective optimization, particle swarm optimization, soft computing

DOI: 10.3233/JIFS-17336

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 3, pp. 1577-1588, 2017

Authors: Kobza, Vladimír | Janiš, Vladimír | Montes, Susana

Article Type: Research Article

Abstract: Measures of difference are studied for hesitant fuzzy sets, i.e. mappings where the values are multisets in the unit interval. Locality (the change of the value of the difference is dependent only on changes in singletons) of such mappings is discussed and the class of all local divergences is characterized. Entropies for hesitant fuzzy sets are also studied, namely fuzziness entropy measure and hesitance entropy measure.

Keywords: Hesitant fuzzy set, divergence measure, hesitant divergence measure, locality, entropy

DOI: 10.3233/JIFS-161430

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 3, pp. 1589-1601, 2017

Authors: Fan, Zongwen | Gou, Jin | Wang, Cheng | Luo, Wei

Article Type: Research Article

Abstract: The airfoil noise problem is highly nonlinear, but its prediction is very important to broadband helicopter rotors, wind turbines, and airframe noise. Thus, this paper presents a novel strategy whereby the minimum-of-maximum relative error support vector machine (RE-SVM) is used to improve the approximation ability of a fuzzy airfoil noise prediction system. In the preliminary design stage, the antecedents of the fuzzy rule base are used to cluster the fuzzy rules. Then, those fuzzy rules with the same antecedent are clustered. Next, in each cluster, the fuzzy rule that has the highest degree of confidence is regarded as the cluster …center, which becomes the final fuzzy rule. Finally, the consequents of the fuzzy rules are obtained using RE-SVM models. The prediction of airfoil noise demonstrates that the proposed method has high prediction accuracy. Show more

Keywords: Fuzzy model identification, fuzzy-rule clustering, minimum-of-maximum relative error support vector machine (RE-SVM), approximation ability

DOI: 10.3233/JIFS-17227

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 3, pp. 1603-1611, 2017

Authors: Javidi, Mohammad Masoud | Eskandari, Sadegh

Article Type: Research Article

Abstract: The aim of feature selection (FS) is to select a small subset of most important and discriminative features. Many FS approaches based on rough set theory up to now, have employed reduct analysis using feature dependency measures. However the critical shortcoming for such approaches is that they are not able to manage useful information that may be destroyed by noise elements. Therefore several extensions to the original theory have been proposed. Three notable extensions are fuzzy rough set (FRS), variable precision rough set (VPRS), and tolerance rough set model (TRSM). Although successful, each of the extensions exhibits a critical shortcoming …which makes that extension inapplicable in most of scenarios. For example, FRS is able to describe the existing dependencies between different attributes accurately, but its high run-times makes it inapplicable to larger datasets. As another example, VPR is very fast, but requires more information than contained within the data itself, which is inaccessible for most of the applications. This paper examines a rough set FS technique which uses a noise resistant dependency measure to quantify information that may be hidden due to the noise elements. Experimental results demonstrate that the use of this measure can result more discriminative reducts than those obtained using other RSFS approaches. Moreover, the proposed measure is as fast as VPRS and as accurate as FRS and TRSM, while it need no additional information other than contained within the data. Show more

Keywords: Feature selection, rough sets theory, impurity measure, noise resistant dependency

DOI: 10.3233/JIFS-16853

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 3, pp. 1613-1626, 2017

Authors: Keshavarz Ghorabaee, Mehdi | Amiri, Maghsoud | Zavadskas, Edmundas Kazimieras | Turskis, Zenonas | Antucheviciene, Jurgita

Article Type: Research Article

Abstract: Discrete stochastic multi-criteria decision-making (MCDM) can be used to handle many real-life decision-making problems. The Evaluation Based on Distance from Average Solution (EDAS) is a new and efficient MCDM method. The desirability of alternatives in this method is determined based on distances of them from an average solution. Because the average solution is determined by an arithmetic mean in this method, the EDAS method can be efficient for solving stochastic problems. In this paper, a stochastic EDAS method is proposed to handle problems in which the performance values of alternatives on each criterion follow the normal distribution. Based on the …proposed method, we can obtain optimistic and pessimistic appraisal scores for evaluation of alternatives and consider the uncertainty of decision-making data. We present a graphical example to illustrate the proposed method and a practical example of performance evaluation of bank branches to show the applicability of it. According to the analyses made, the proposed method is efficient and the results are valid. Show more

Keywords: Multi-criteria decision-making, MCDM, stochastic MCDM, EDAS, normal distribution

DOI: 10.3233/JIFS-17184

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 3, pp. 1627-1638, 2017

Authors: Adinehvand, Karim | Sardari, Dariush | Hosntalab, Mohammad | Pouladian, Majid

Article Type: Research Article

Abstract: Digital retinal images are commonly used for hard exudates and lesion detection. An efficient segmentation method is needed to detect and discern the lesions from the retinal area. In this paper, a hybrid method is presented for digital retinal image processing for diagnosis and screening purposes. The goal of this research is to suggest a supervised/semi supervised approach for exudates detection in fundus images and it is also to investigate a technique to find the optimum structure. The image is first transformed into fuzzy domain after an initialization. A cellular learning automata model is used to detect any abnormality on …the image which is related to a lesion. The automaton is created with an extra term as the rule updating term to increase the flexibility and capability of the cellular automata. The selection and updating of rule are implemented automatically We also performed allocating the score and penalty value for the cells toward the process of segmentation Three main statistical criteria are introduced as the sensitivity, specificity and accuracy. A number of 50 retinal images with visually detection hard exudates and lesions are the experimental dataset for evaluation and validation of the method. For STARE retina image dataset, for a neighborhood of 5 × 5, score of ϑ  = 0.01, penalty of ξ  = 0.01, ratio of state overall variation in three sequential cycles in cellular automata η ¯ = 0.5 , updating additive value σ  = 0.02 & rule selection threshold value ρ  = 0.8 the mean value of statistical criteria averaged over all dataset can reach 99% which is an outstanding assessment result for the proposed method. Show more

Keywords: Digital retinal images, hard exudates and lesions detection, fuzzy theory, cellular learning automata, statistical evaluation

DOI: 10.3233/JIFS-17199

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 3, pp. 1639-1649, 2017

Authors: Zhou, Wei | Ding, Bingqing | Zhang, Ying | Bush, Anthony

Article Type: Research Article

Abstract: To address the issues of insufficient utilization of data and fixed structure of grey model [GM (1,1)], this paper develops an exponential buffer operator based on the new information and variable parameter principles. Measuring and modifying the fluctuation trend of original data is an obvious advantage of this new buffer operator. Further, we prove the weakening, smoothness, new-information, and incremental-innovation properties of the exponential buffer operator. Then, the improved GM (1,1) model is proposed by combining the GM (1,1) model with the exponential buffer operator. This new model combines the fitting advantages of the GM (1,1) model in small sample …environment and the additional advantages of the buffer operator of dealing with disturbance factors. Also, we compare the proposed buffer operators with the general buffer operator and the improved GM (1,1) model with the GM (1,1) model. It is found that not only the improved GM (1,1) model can effectively weaken the fluctuation trend in original data sequence, it also reduces forecasting errors and improves the calculation accuracy under the fluctuation small-sample environment. Finally, based on an empirical forecasting of the coal consumption in China, we demonstrate the feasibility and effectiveness of the improved GM (1,1) model and exponential buffer operator. Show more

Keywords: Grey system theory, exponential buffer operator, improved GM (1,1) model, new information, coal consumption

DOI: 10.3233/JIFS-17419

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 3, pp. 1651-1663, 2017

Authors: Gong, Zengtai | Wang, Qian

Article Type: Research Article

Abstract: The task of classification has been widely researched with many approaches proposed in the literature, several methods are available for the automatic definition of fuzzy classification. Meanwhile fuzzy formal concept analysis, fuzzy information system and fuzzy hypergraphs are three well-developed fields of study. In this paper, we researched fuzzy classification through connecting these three topics, and the equivalence relationship between fuzzy hypergraphs and fuzzy formal concept analysis and fuzzy information systems. At last, the particular case of completek -uniform fuzzy hypergraphs was studied. More in detail, we give the definition of indiscernibility relation generated by attributes of fuzzy information system. …Based on this, we compute the indiscernibility class of fuzzy information system and the set of reducts. Show more

Keywords: Fuzzy formal concept analysis, fuzzy information system, fuzzy hypergraphs

DOI: 10.3233/JIFS-16468

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 3, pp. 1665-1676, 2017

Authors: Qiao, Chen | Sun, Kefeng | Jing, Wenfeng | Shi, Yan

Article Type: Research Article

Abstract: Critical dynamics research of recurrent neural networks (RNNs) is very meaningful in both theoretical importance and practical significance. Recently, because of the application requirements, the study on the critical dynamics behaviors of RNNs has drawn special attention. The critical condition is that a discriminant matrix M 1  (Γ ) related with an RNN is nonnegative definite. Due to the essential difficulty in analysis, there were only a few critical results up to now. Further, nearly all of the existing dynamic results are with diagonally nonlinear requirements on the activation mappings, i.e., the activation mapping G should satisfy the strict …necessary condition that G  (x ) = (g 1  (x 1 ) , g 2  (x 2 ) , ⋯ , g N  (x N )) T . This is because of the essential difficulty on the analysis of the energy function. The requirement is so strict and it limits the applications of RNNs. In this paper, under the critical conditions, some new global asymptotically stable conclusions are presented for RNNs without the diagonally nonlinear requirement on the activation mappings. The results present here not only improve substantially upon the existing relevant critical stability results, but also provide some further cognizance on the essentially dynamical behavior of RNNs, and further, enlarge the application fields of them. Show more

Keywords: Without diagonal nonlinear, critical dynamics analysis, global asymptotically stable, recurrent neural networks

DOI: 10.3233/JIFS-16986

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 3, pp. 1677-1685, 2017