Title :
Approximation capability to functions of several variables, nonlinear functionals, and operators by radial basis function neural networks
Author :
Chen, Tianping ; Chen, Hong
Author_Institution :
Dept. of Math., Fudan Univ., Shanghai, China
fDate :
7/1/1995 12:00:00 AM
Abstract :
The purpose of this paper is to explore the representation capability of radial basis function (RBF) neural networks. The main results are: 1) the necessary and sufficient condition for a function of one variable to be qualified as an activation function in RBF network is that the function is not an even polynomial, and 2) the capability of approximation to nonlinear functionals and operators by RBF networks is revealed, using sample data either in frequency domain or in time domain, which can be used in system identification by neural networks
Keywords :
approximation theory; feedforward neural nets; function approximation; identification; activation function; function approximation; necessary condition; nonlinear functionals; radial basis function neural networks; sufficient condition; system identification; Feedforward neural networks; Frequency domain analysis; Kernel; Multi-layer neural network; Multilayer perceptrons; Neural networks; Polynomials; Radial basis function networks; Sufficient conditions; System identification;
Journal_Title :
Neural Networks, IEEE Transactions on
