Orthogonal polynomials neural network for function approximation and system modeling
Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45) › 32_Refereed conference paper (with ISBN/ISSN) › peer-review
Author(s)
Detail(s)
| Original language | English |
|---|---|
| Title of host publication | IEEE International Conference on Neural Networks - Conference Proceedings |
| Pages | 594-599 |
| Volume | 1 |
| Publication status | Published - 1995 |
| Externally published | Yes |
Publication series
| Name | |
|---|---|
| Volume | 1 |
Conference
| Title | Proceedings of the 1995 IEEE International Conference on Neural Networks. Part 1 (of 6) |
|---|---|
| City | Perth, Aust |
| Period | 27 November - 1 December 1995 |
Link(s)
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 Works (RGC: 12, 32, 41, 45) › 32_Refereed conference paper (with ISBN/ISSN) › peer-review
