Best approximation to C2 functions and its error bounds using regular-center gaussian networks

Binfan Liu, Jennie Si

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Gaussian neural networks are considered to approximate any C2 function with support on the unit hypercube Im = [0,1]m in the sense of best approximation. An upper bound (O(N-2)) of the approximation error is obtained in the present paper for a Gaussian network having Nm hidden neurons with centers defined on a regular mesh in Im.

Original languageEnglish (US)
Title of host publicationIEEE International Conference on Neural Networks - Conference Proceedings
Place of PublicationPiscataway, NJ, United States
PublisherIEEE
Pages2400-2406
Number of pages7
Volume4
StatePublished - 1994
EventProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) - Orlando, FL, USA
Duration: Jun 27 1994Jun 29 1994

Other

OtherProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7)
CityOrlando, FL, USA
Period6/27/946/29/94

ASJC Scopus subject areas

  • Software

Fingerprint Dive into the research topics of 'Best approximation to C<sup>2</sup> functions and its error bounds using regular-center gaussian networks'. Together they form a unique fingerprint.

  • Cite this

    Liu, B., & Si, J. (1994). Best approximation to C2 functions and its error bounds using regular-center gaussian networks. In IEEE International Conference on Neural Networks - Conference Proceedings (Vol. 4, pp. 2400-2406). IEEE.