Orthogonal polynomials neural network for function approximation and system modeling

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

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Author(s)

Detail(s)

Original languageEnglish
Title of host publicationIEEE International Conference on Neural Networks - Conference Proceedings
Pages594-599
Volume1
Publication statusPublished - 1995
Externally publishedYes

Publication series

Name
Volume1

Conference

TitleProceedings of the 1995 IEEE International Conference on Neural Networks. Part 1 (of 6)
CityPerth, Aust
Period27 November - 1 December 1995

Abstract

By using a series of orthogonal polynomials, the architecture of a neural network can be developed for function approximation and system modeling. Due to the orthogonality properties, the regression matrix for parameter estimation is not of column degeneracy and the magnitude of estimated parameters is small. This make the proposed neural network be useful in practical applications. Orthogonal least squares technique is applied for parameter estimation and model structure. The neural network can be constructed to meet some pre-specified root mean square error in one pass. Some simulations are done to support and illustrate our approach.

Citation Format(s)

Orthogonal polynomials neural network for function approximation and system modeling. / Chak, Chu Kwong; Feng, Gang; Cheng, Chi Ming.
IEEE International Conference on Neural Networks - Conference Proceedings. Vol. 1 1995. p. 594-599.

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review