A design of dynamically simultaneous search GA-based fuzzy neural networks: comparative analysis of FS and FR-based rules

  • Authors:

  • Sung-Kwun Oh;Wook-Dong Kim;Byoung-Jun Park

  • Affiliations:

  • The University of Suwon, Hwaseong-si, Gyeonggi-do, South Korea;The University of Suwon, Hwaseong-si, Gyeonggi-do, South Korea;Electronics and Telecommunications Research Institute, Daejeon, South Korea

  • Venue:
  • Proceedings of the 2011 ACM Symposium on Research in Applied Computation
  • Year:
  • 2011

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Abstract

In this paper, we introduce advanced architectures of dynamic search GA-oriented Fuzzy Neural Networks (FNNs) based on fuzzy set and fuzzy relation and discuss a comprehensive design methodology. The proposed FNNs are based on 'if-then' rule-based networks with the extended structure of the premise and the consequence parts of the fuzzy rules. We consider two types of the FNNs topologies, called here FSNNs and FRNNs, depending upon the usage of inputs in the premise of fuzzy rules. Three different type of polynomials function (namely, constant, linear, and quadratic) are used to construct the consequence of the rules. In order to improve the accuracy of FNNs, the structure and the parameters are optimized by making use of genetic algorithms (GAs). We enhance the search capabilities of the GAs by introducing the dynamic variants of genetic optimization. It fully exploits the processing capabilities of the FNNs by supporting their structural and parametric optimization. To evaluate the performance of the proposed FNNs, we exploit a suite of several representative numerical examples and its experimental results are compared with those reported in the previous studies.