TY - GEN
T1 - Orthogonal polynomials neural network for function approximation and system modeling
AU - Chak, Chu Kwong
AU - Feng, Gang
AU - Cheng, Chi Ming
PY - 1995
Y1 - 1995
N2 - 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.
AB - 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.
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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-0029487301&origin=recordpage
M3 - RGC 32 - Refereed conference paper (with host publication)
VL - 1
SP - 594
EP - 599
BT - IEEE International Conference on Neural Networks - Conference Proceedings
T2 - Proceedings of the 1995 IEEE International Conference on Neural Networks. Part 1 (of 6)
Y2 - 27 November 1995 through 1 December 1995
ER -