Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Structure Identification of a Neuro-Fuzzy Model Can Reduce Inconsistency of Its Rulebase

  • Wang, Bo-Hyeun (Department of Electrical Engineering, Kangnung National University) ;
  • Cho, Hyun-Joon (Department of Electrical and Computer Engineering, Purdue University)
  • Published : 2007.04.25
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Abstract

It has been shown that the structure identification of a neuro-fuzzy model improves their accuracy performances in a various modeling problems. In this paper, we claim that the structure identification of a neuro-fuzzy model can also reduce the degree of inconsistency of its fuzzy rulebase. Thus, the resulting neuro-fuzzy model serves as more like a structured knowledge representation scheme. For this, we briefly review a structure identification method of a neuro-fuzzy model and propose a systematic method to measure inconsistency of a fuzzy rulebase. The proposed method is applied to problems or fuzzy system reproduction and nonlinear system modeling in order to validate our claim.

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