[1]LI Wei,QIAO Junfei,HAN Honggui,et al.Computing and performance analysis of similarity between fuzzy rules[J].CAAI Transactions on Intelligent Systems,2017,12(1):124-131.[doi:10.11992/tis.201512040]
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Computing and performance analysis of similarity between fuzzy rules

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 12 Number of periods: 2017 1 Page number: 124-131 Column: 学术论文—机器学习 Public date: 2017-02-25
Title:
Computing and performance analysis of similarity between fuzzy rules
Author(s):
LI Wei1; 2;  QIAO Junfei1; 2;  HAN Honggui1; 2;  ZENG Xiaojun3
1. Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China;
2. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China;
3. School of Computer Science, the University of Manchester, Manchester M13 9PL, UK
Keywords:
fuzzy rules; similarity computing; distinguishability; dimension dependency; computing complexity
CLC:
TP18
DOI:
10.11992/tis.201512040
Abstract:
Facing the weaknesses of the existing analysis and computing methods for the similarity between fuzzy rules (FRs), this paper investigated the computing methods for the similarity between FRs. First, the similarity between FSs was transferred equivalently into the similarity between multivariable fuzzy sets, and then three application based performance criterions-distinguishability, dimension dependency, and computing complexity were proposed to evaluate the computing methods of the similarity between FRs. Second, four new methods were proposed based on the two existing methods for computing the similarity between FRs, and then the performance analysis and comparison between these new and existing methods were performed. Next, a simulation example for the similarity computing between FRs was provided, and the simulation shows effectiveness of the proposed performance criteria, feasibility of the computing methods, and correctness of the analysis conclusions. The results obtained in this paper provide powerful tools and guides for the similarity analysis and computing of FRs. Inparticular, they establish the methodological foundation and provide a new design approach for the merging of similar FRs in the structure simplification of fuzzy systems and fuzzy neural networks.
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