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http://dx.doi.org/10.5391/JKIIS.2007.17.2.276

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)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.17, no.2, 2007 , pp. 276-283 More about this Journal
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.
Keywords
Neuro-fuzzy modeling; structure identification; measure of inconsistency; input space partitioning; genetic algorithm;
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