Comparison of neural models, off-line and on-line learning algorithms for a benchmark problem

被引：0

作者：

Ruano, AEB ^{[1
]}

机构：

[1] Univ Algarve, FCT, CSI, P-8000 Faro, Portugal

来源：

ARTIFICIAL NEURAL NETS PROBLEM SOLVING METHODS, PT II | 2003年 / 2687卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper compares the application of different neural models multilayer perceptrons, radial basis functions and B-splines - for a benchmark problem, and illustrates the applicability of a common learning algorithm for all models considered. The learning algorithm is employed both for off-line training and for on-line model adaptation. In the latter case, a sliding window of past learning data is employed.

引用

页码：457 / 464

页数：8

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