This paper presents a training method for radial basis function neural network (RBFNN) based on GA and dynamic optimal learning rate. Genetic algorithm (GA) is applied to search for the optimal centers from input space of the RBF. Dynamic optimal learning rate approach is also appended to the genetic algorithms’ searching procession to obtain a set of weighting factors so that the network actua...

We describe here the application of a type of artificial neural network, the Gaussian radial basis function (RBF) network, in the identification of a large number of phytoplankton strains from their 11-dimensional flow cytometric characteristics measured by the European Optical Plankton Analyser instrument. The effect of network parameters on optimization is examined. Optimized RBF networks rec...

In this paper we have studied adaptive equalization in the GSM (Global System for Mobile communications) environment using radial basis function (RBF) networks. Equalization is here considered as a classification problem, where the idea is to map the received complex-valued signal into desired binary values using RBF network equalizer. Results prove that the RBF network provides very good bit e...

Using radial basis function networks for function approximation tasks suffers from unavailable knowledge about an adequate network size. In this work, a measuring technique is proposed which can control the model complexity and is based on the correlation coefficient between two basis functions. Simulation results show good performance and, therefore, this technique can be integrated in the RBF...

Conventionally, in radial basis function (RBF) network width factor is constructed by obtaining r-nearest neighbor rule or taking equal to a constant for all Gaussian functions. This paper proposes an approach for the construction of width factor using genetic algorithm to optimize the Gaussian function. Our experimental results show that our proposed optimal-based width outperforms the convent...

The value algorithms of classical function approximation theory have a common drawback: the compute-intensive, poor adaptability, high model and data demanding and the limitation in practical applications. Neural network can calculate the complex relationship between input and output, therefore, neural network has a strong function approximation capability. This paper describes the application ...

This document is an introduction to radial basis function RBF networks a type of arti cial neural network for application to problems of supervised learning e g regression classi cation and time series prediction It is now only available in PostScript an older and now unsupported hyper text ver sion may be available for a while longer The document was rst published in along with a package of Ma...