The Best Approximation to C2 Functions and its Error Bounds Using Regular-Center Gaussian Networks

Binfan Liu; Jennie Si

doi:10.1109/72.317739

The Best Approximation to C2 Functions and its Error Bounds Using Regular-Center Gaussian Networks

Binfan Liu, Jennie Si

Electrical Engineering

Research output: Contribution to journal › Article › peer-review

24 Scopus citations

Abstract

Gaussian neural networks are considered to approximate any C2function with support on the unit hypercube Im= [0,1]min the sense of best approximation. An upper bound (O(N–2)) of the approximation error is obtained in the present letter for a Gaussian network having N m hidden neurons with centers defined on a regular mesh in Im.

Original language	English (US)
Pages (from-to)	845-847
Number of pages	3
Journal	IEEE Transactions on Neural Networks
Volume	5
Issue number	5
DOIs	https://doi.org/10.1109/72.317739
State	Published - Sep 1994

ASJC Scopus subject areas

Software
Computer Science Applications
Computer Networks and Communications
Artificial Intelligence

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10.1109/72.317739

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title = "The Best Approximation to C2 Functions and its Error Bounds Using Regular-Center Gaussian Networks",

abstract = "Gaussian neural networks are considered to approximate any C2function with support on the unit hypercube Im= [0,1]min the sense of best approximation. An upper bound (O(N–2)) of the approximation error is obtained in the present letter for a Gaussian network having N m hidden neurons with centers defined on a regular mesh in Im.",

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AB - Gaussian neural networks are considered to approximate any C2function with support on the unit hypercube Im= [0,1]min the sense of best approximation. An upper bound (O(N–2)) of the approximation error is obtained in the present letter for a Gaussian network having N m hidden neurons with centers defined on a regular mesh in Im.

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The Best Approximation to C2 Functions and its Error Bounds Using Regular-Center Gaussian Networks

Abstract

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