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

  • Noritaka Shigei
  • Hiromi Miyajima
  • Shinya Nagamine
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5552)

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.

Keywords

Fuzzy reasoning model Single-input rule module Small-number-of-input rule module A large number of input variables 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Noritaka Shigei
    • 1
  • Hiromi Miyajima
    • 1
  • Shinya Nagamine
    • 1
  1. 1.Kagoshima UniversityKagoshimaJapan

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