A Proposal of Fuzzy Inference Model Composed of Small-Number-of-Input Rule Modules

  • Authors:

  • Noritaka Shigei;Hiromi Miyajima;Shinya Nagamine

  • Affiliations:

  • Kagoshima University, Kagoshima, Japan 890-0065;Kagoshima University, Kagoshima, Japan 890-0065;Kagoshima University, Kagoshima, Japan 890-0065

  • Venue:
  • ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
  • Year:
  • 2009

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Abstract

The automatic construction of fuzzy system with a large number of input variables involves many difficulties such as large time complexities and getting stuck in a shallow local minimum. In order to overcome them, an SIRMs (Single-Input Rule Modules) model has been proposed. However, such a simple model does not always achieve good performance in complex non-linear systems. This paper proposes a fuzzy reasoning model as a generalized SIRMs model, in which each module deals with a small number of input variables. The reasoning output of the model is determined as the weighted sum of all modules, where each weight is the importance degree of a module. Further, in order to construct a simpler model, we introduce a module deletion function according to the importance degree into the proposed system. With the deletion function, we propose a learning algorithm to construct a fuzzy reasoning system consisting of small-number-of-input rule modules (SNIRMs). The conducted numerical simulation shows that the proposed method is superior in terms of accuracy compared to the conventional SIRMs model.