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An orthogonal least-squares method for recurrent fuzzy-neural modeling

✍ Scribed by Paris A. Mastorocostas; John B. Theocharis

Publisher: Elsevier Science
Year: 2003
Tongue: English
Weight: 411 KB
Volume: 140
Category: Article
ISSN: 0165-0114
DOI: 10.1016/s0165-0114(02)00523-7

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✦ Synopsis

This paper presents an orthogonal least-squares (OLS)-based modeling method, named dynamic OLS (D-OLS), for generating recurrent fuzzy models. A dynamic-neuron-based fuzzy neural network is proposed, comprising generalized Takagi-Sugeno-Kang (TSK) fuzzy rules, whose consequent parts consist of dynamic neurons with local output feedback. From an arbitrarily large set of candidate dynamic neurons, the D-OLS method selects automatically the most important ones. Thus, in the resulting model, the consequent part of each fuzzy rule contains dynamic neurons with di erent time delays. The proposed dynamic model, equipped with the learning algorithm, is applied to two temporal problems, where the e ectiveness of the suggested method as well as the advantages of the resulting dynamic model are demonstrated.

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