Genetically optimized rule-based fuzzy polynomial neural networks: synthesis of computational intelligence technologies

  • Authors:

  • Sung-Kwun Oh;James F. Peters;Witold Pedrycz;Tae-Chon Ahn

  • Affiliations:

  • Department of Electrical Electronic and Information Engineering, Wonkwang University, Iksan, Chon-Buk, South Korea;Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, Manitoba, Canada;Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada and Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland;Department of Electrical Electronic and Information Engineering, Wonkwang University, Iksan, Chon-Buk, South Korea

  • Venue:
  • RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
  • Year:
  • 2003

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Abstract

In this study, we introduce a concept of Rule-based fuzzy polynomial neural networks(RFPNN), a hybrid modeling architecture combining rule-based fuzzy neural networks(RFNN) and polynomial neural networks(PNN). We discuss their comprehensive design methodology. The development of the RFPNN dwells on the technologies of Computational Intelligence(CI), namely fuzzy sets, neural networks, and genetic algorithms. The architecture of the RFPNN results from a synergistic usage of RFNN and PNN. RFNN contribute to the formation of the premise part of the rule-based structure of the RFPNN. The consequence part of the RFPNN is designed using PNN. We discuss two kinds of RFPNN architectures and propose a comprehensive learning algorithm. In particular, it is shown that this network exhibits a dynamic structure. The experimental results include well-known software data such as the Medical Imaging System(MIS) dataset.