Identification of Numerically Accurate First-Order Takagi-Sugeno Systems with Interpretable Local Models from Data

Andri Riid; Ennu Rüstern

doi:10.20965/jaciii.2005.p0526

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JACIII Vol.9 No.5 pp. 526-533

doi: 10.20965/jaciii.2005.p0526

(2005)

Paper:

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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, 2004

Accepted:

June 12, 2005

Published:

September 20, 2005

Keywords:

fuzzy system, inference mechanism, modeling, interpolation

Abstract

The paper deals with the interpretability problem of 1^st 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.

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