A fast training algorithm for RBF networks based on subtractive clustering

NeurocomputingPublished 15 March 2003Open access

Haralambos Sarimveis, Alex Alexandridis, George Bafas

Citations68

TL;DR

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, which proves suitable for developing models for complex nonlinear systems.

Abstract

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.

Keywords

Computer ScienceEngineering

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