Journal of Intelligent & Fuzzy Systems - Volume 19, issue 3

Authors: Pal, Nikhil Ranja | Sharma, Animesh | Sanadhya, Somitra Kumar

Article Type: Research Article

Abstract: Although a fuzzy rule based system offers interpretability, its application in gene expression data analysis becomes difficult due to the very high dimensional nature of the data. Here we propose an interesting scheme of combining fuzzy modeling with neural networks for designing fuzzy rule based classifiers for gene expression data analysis. A neural system is used for selecting a set of informative genes. Considering only these selected set of genes, we cluster the expression data with …a fuzzy clustering algorithm. Each cluster is then converted into a fuzzy if-then rule, which models an area in the input space. These rules are tuned using a gradient descent technique to improve the classification performance. The rule base is tested on a leukemia data set containing two classes and it is found to produce good results. We propose some simple criteria to simplify membership functions and the rules. Our rule extraction scheme can be automated. Unlike other classifiers, it produces human interpretable rules which are not expected to give poor generalization because fuzzy rules do not respond to areas not represented by the training data. The last two properties are very important for problems like diagnosis of cancer. Show more

Citation: Journal of Intelligent & Fuzzy Systems, vol. 19, no. 3, pp. 171-180, 2008

Authors: Davvaz, Bijan | Zhan, Jianming | Shum, K.P.

Article Type: Research Article

Abstract: In this paper, the concept of quasi-coincidence of a fuzzy interval value with an interval valued fuzzy set, which is a generalization of quasi-coincidence of a fuzzy point with a fuzzy set, is introduced. Using this new idea, the notion of interval valued (α,β)-fuzzy sub-polygroups in a polygroup, which is a generalization of a fuzzy sub-polygroup, is defined, and related properties are investigated. In particular, the study of interval valued (∈,∈∨q)-fuzzy sub-polygroups in a polygroup is …dealt with. Finally, we consider the concept of implication-based interval valued fuzzy sub-polygroups in a polygroups. Show more

Keywords: Polygroup, interval valued (α,β)-fuzzy sub-polygroup, interval valued (∈,∈∨q)-fuzzy sub-polygroup, fuzzy logic, implication operator

Citation: Journal of Intelligent & Fuzzy Systems, vol. 19, no. 3, pp. 181-188, 2008

Authors: Hatzimichailidis, Anestis G. | Papadopoulos, Basil K.

Article Type: Research Article

Abstract: In this paper we introduce a specific method, which is based on generalized modus ponens and generalized modus tollens, for introducing an ordering relation on the set of fuzzy implications. The existence of an ordering relation between fuzzy implications helps us to order fuzzy implications in a more powerful and/or more functional classes. We believe that, apart from the theoretical interest, this method appears to have interesting applications.

Keywords: Fuzzy implication, ordering relation, fuzzy sets

Citation: Journal of Intelligent & Fuzzy Systems, vol. 19, no. 3, pp. 189-195, 2008

Authors: Li, Xiang | Liu, Baoding

Article Type: Research Article

Abstract: Distance between fuzzy variables has been defined in many ways, for example, Hausdorff-like distance and Hamming distance. This paper discusses the properties of a new distance between fuzzy variables defined by expected value operator. Furthermore, a metric space of fuzzy variables is defined and the completeness of this space is proved. Finally, the distances between fuzzy vectors, fuzzy random vectors, and random fuzzy vectors are given.

Keywords: Fuzzy variable, distance, fuzzy random variable, random fuzzy variable

Citation: Journal of Intelligent & Fuzzy Systems, vol. 19, no. 3, pp. 197-204, 2008

Authors: Baykasoǧlu, Adil | Göçken, Tolunay

Article Type: Research Article

Abstract: In this article, a review of fuzzy mathematical programming models according to fuzzy components is given. Fifteen types of the fuzzy mathematical programming models are identified in the proposed classification. All possible combinations of fuzzy components are considered in classifying fuzzy mathematical programs. Existing solution procedures which were proposed in the literature for solving fuzzy mathematical programs are also reviewed and discussed in the paper.

Keywords: Fuzzy mathematical programming, classification, fuzzy components

Citation: Journal of Intelligent & Fuzzy Systems, vol. 19, no. 3, pp. 205-229, 2008