A fast training algorithm for RBF networks based on subtractive clustering

被引：54

作者：

Sarimveis, H ^{[1
]}

Alexandridis, A ^{[1
]}

Bafas, G ^{[1
]}

机构：

[1] Natl Tech Univ Athens, Sch Chem Engn, Athens 15780, Greece

来源：

NEUROCOMPUTING | 2003年 / 51卷

关键词：

radial basis function networks; training algorithms; model selection;

D O I：

10.1016/S0925-2312(03)00342-4

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 algorithm for training radial basis function neural networks is presented in this paper. The algorithm, which is based on the subtractive clustering technique, has a number of advantages compared to the traditional learning algorithms, including faster training times and more accurate predictions. Due to these advantages the method proves suitable for developing models for complex nonlinear systems. (C) 2003 Elsevier Science B.V. All rights reserved.

引用

页码：501 / 505

页数：5

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