Approximation capability to functions of several variables, nonlinear functionals, and operators by radial basis function neural networks 论文

详细信息

发表期刊/会议

IEEE Transactions on Neural Networks

发表日期

1995-07-01

发表年份

1995

摘要

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.