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JACIII Vol.16 No.5 pp. 653-661
doi: 10.20965/jaciii.2012.p0653
(2012)

Paper:

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Modeling Approach Based on Modular Fuzzy Model

Toshihiko Watanabe* and Hirosato Seki**

*Department of Electrical and Electronic Engineering, Faculty of Engineering, Osaka Electro-Communication University, 18-8 Hatsu-cho, Neyagawa, Osaka 572-8530, Japan

**Department of Mathematical Sciences, Kwansei Gakuin University, 2-1 Gakuen, Sanda, Hyogo 669-1337, Japan

Received:
December 19, 2011
Accepted:
May 30, 2012
Published:
July 20, 2012
Creative Commons License
Keywords:
fuzzy modeling, modular fuzzy model, SIRMs, reinforcement learning
Abstract
Fuzzy modeling is one of the most important techniques for nonlinear modeling. SIRMs (Single Input Rule Modules) has been studied as a useful modeling method for real-life applications such as control and pattern recognition. Although the SIRMs is a practical modeling approach based on fuzzy reasoning, its performance is adversely affected by high-dimensional or complicated characteristics of the problems. The modular fuzzy model is an extension of the SIRMs for overcoming such a performance problem. In this paper, we study a modeling approach based on the modular fuzzy model by extending the SIRMs architecture. We show that the construction of error objective functions for modeling the modular fuzzy model and the SIRMs affects the prediction performance of the model. Through numerical experiments on modeling problems and reinforcement learning problems, we study the model construction based on the error objective functions. We find that the error objective function should be selected according to the number of dimensions of projection in the modular fuzzy model.
Cite this article as:
T. Watanabe and H. Seki, “Modeling Approach Based on Modular Fuzzy Model,” J. Adv. Comput. Intell. Intell. Inform., Vol.16 No.5, pp. 653-661, 2012.
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