On-line supervised adaptive training using radial basis function networks

被引：27

作者：

Fung, CF ^{[1
]}

Billings, SA ^{[1
]}

Luo, W ^{[1
]}

机构：

[1] UNIV NEWCASTLE UPON TYNE, NEWCASTLE UPON TYNE NE1 7RU, TYNE & WEAR, ENGLAND

来源：

NEURAL NETWORKS | 1996年 / 9卷 / 09期

基金：

英国工程与自然科学研究理事会;

关键词：

radial basis function network; neural network learning algorithm; parameter estimation; adaptive filtering; system identification; dynamical system modelling; model selection; pattern recognition;

D O I：

10.1016/S0893-6080(96)00024-X

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A new recursive supervised training algorithm is derived for the radial basis neural network architecture. The new algorithm combines the procedures of on-line candidate regressor selection with the conventional Givens QR based recursive parameter estimator to provide efficient adaptive supervised network training. A new concise on-line correlation based performance monitoring scheme is also introduced as an auxiliary device to detect structural changes in temporal data processing applications. Practical and simulated examples are included to demonstrate the effectiveness of the new procedures. Copyright (C) 1996 Elsevier Science Ltd.

引用

页码：1597 / 1617

页数：21

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