Release Time:2019-03-10  Hits:

Indexed by: Journal Article

Date of Publication: 2008-09-01

Journal: ACTA MATHEMATICA SINICA-ENGLISH SERIES

Included Journals: Scopus、SCIE、CSCD、ISTIC

Volume: 24

Issue: 9

Page Number: 1533-1540

ISSN: 1439-8516

Key Words: neural networks; radial basis function; L(p) approximation capability

Abstract: L(p) approximation capability of radial basis Function (RBF) neural networks is investigated. If g : R(+)(1) -> R(1) and g(parallel to x parallel to(n)(R)) epsilon L(loc)(p)(R(n)) with 1 <= p <= infinity, then the RBF neural networks with g as the activation function can approximate any given function in LP(K) with any accuracy for any compact set K in R(n), if and only if g(x) is not an even polynomial.