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

被引：1

作者：

Vachkov, GL ^{[1
]}

Kiyota, Y ^{[1
]}

Komatsu, K ^{[1
]}

机构：

[1] Kagawa Univ, Fac Engn, Dept Reliabil Based Informat Syst Engn, Kagawa 7610396, Japan

来源：

NAFIPS 2005 - 2005 Annual Meeting of the North American Fuzzy Information Processing Society | 2005年

关键词：

D O I：

10.1109/NAFIPS.2005.1548550

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：292 / 297

页数：6

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