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

Approximation capability to functions of several variables, nonlinear functionals, and operators by radial basis function neural networks. Research Abstract Details 

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Approximation capability to functions of several variables, nonlinear functionals, and operators by radial basis function neural networks. Abstract Text:

T Chen,H Chen,

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.

Approximation capability to functions of several variables, nonlinear functionals, and operators by radial basis function neural networks. Publishing Authors By Initials

T Chen,H Chen,

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Approximation capability to functions of several variables, nonlinear functionals, and operators by radial basis function neural networks. Journal Published:

PUBLICATION TYPE: Journal Article

Journal: IEEE transactions on neural networks / a publicati

VOLUME: 6

Page Numbers: 904-10

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ISSN: 1045-9227

DAY: 11

MONTH: 02

YEAR: 1995

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

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LANGUAGE: eng

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Grant and Affiliation Information for Approximation capability to functions of several variables, nonlinear functionals, and operators by radial basis function neural networks.

AFFILIATION: Dept. of Math., Fudan Univ., Shanghai.

Country: United States

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MEDLINETA: IEEE Trans Neural Netw

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