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Learning of RBF network models for prediction of unmeasured parameters by use of rules extraction algorithm

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3 Author(s)
Vachkov, G.L. ; Dept. of Reliability-based Inf. Syst. Eng., Kagawa Univ., Takamatsu, Japan ; Kiyota, Y. ; Komatsu, K.

The paper presents three different methods for learning of normalized RBF network models that are similar in structure to the Takagi-Sugeno fuzzy models. These methods use different groups of parameters for optimization and incorporate a rules extraction algorithm for numerical evaluation of the connection weights, as a part of the optimization. Combinations of the methods give different learning strategies, which are analyzed in the paper through two simulated and one real example.

Published in:

Fuzzy Information Processing Society, 2005. NAFIPS 2005. Annual Meeting of the North American

Date of Conference:

26-28 June 2005

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