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Identification of Numerically Accurate First-Order Takagi-Sugeno Systems with Interpretable Local Models from Data
Andri Riid, and Ennu Rüstern
Department of Computer Control, Tallinn University of Technology, Ehitajate tee 5, Tallinn 19086, Estonia
Received:November 29, 2004Accepted:June 12, 2005Published:September 20, 2005
Keywords:fuzzy system, inference mechanism, modeling, interpolation
Abstract
The paper deals with the interpretability problem of 1st order Takagi-Sugeno systems and interpolation issues in particular. Interpolation is improved by a corrective secondary model – essentially a black box – complementing the primary (interpretable) model. Optimization technique for this two-model configuration is developed. Experimental results indicate that the approach achieves a better accuracy-interpretability tradeoff than methodologies currently in use.
Cite this article as:A. Riid and E. Rüstern, “Identification of Numerically Accurate First-Order Takagi-Sugeno Systems with Interpretable Local Models from Data,” J. Adv. Comput. Intell. Intell. Inform., Vol.9 No.5, pp. 526-533, 2005.Data files:
