DocumentCode :
296055
Title :
Orthogonal polynomials neural network for function approximation and system modeling
Author :
Chak, Chu Kwong ; Feng, Gang ; Cheng, Chi Ming
Author_Institution :
Sch. of Electr. Eng., New South Wales Univ., Sydney, NSW, Australia
Volume :
1
fYear :
1995
fDate :
Nov/Dec 1995
Firstpage :
594
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 the estimated parameters is small. This makes the proposed neural network useful in practical applications. The orthogonal least squares technique is applied for parameter estimation and model structuring. The neural network can be constructed to meet some pre-specified root mean square errors in one pass. Some simulations are done to support and illustrate our approach
Keywords :
Legendre polynomials; function approximation; least squares approximations; modelling; neural nets; parameter estimation; polynomials; architecture; function approximation; orthogonal least squares; orthogonal polynomials neural network; parameter estimation; regression matrix; root mean square errors; system modeling; Chebyshev approximation; Function approximation; Least squares approximation; Matrix decomposition; Modeling; Neural networks; Neurons; Parameter estimation; Polynomials; Recurrent neural networks; Root mean square;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1995. Proceedings., IEEE International Conference on
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-2768-3
Type :
conf
DOI :
10.1109/ICNN.1995.488246
Filename :
488246
Link To Document :
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