DocumentCode
2849747
Title
An introduction to radial basis functions for system identification. A comparison with other neural network methods
Author
Warwick, K. ; Craddock, R.
Author_Institution
Dept. of Cybern., Reading Univ., UK
Volume
1
fYear
1996
fDate
11-13 Dec 1996
Firstpage
464
Abstract
A look is taken at the use of radial basis functions (RBFs), for nonlinear system identification. RBFs are firstly considered in detail themselves and are subsequently compared with a multi-layered perceptron (MLP), in terms of performance and usage
Keywords
feedforward neural nets; identification; multilayer perceptrons; nonlinear control systems; multi-layered perceptron; nonlinear system identification; performance; radial basis functions; usage; Approximation methods; Availability; Computational complexity; Control system synthesis; Cybernetics; Ear; Euclidean distance; Neural networks; Nonlinear systems; System identification;
fLanguage
English
Publisher
ieee
Conference_Titel
Decision and Control, 1996., Proceedings of the 35th IEEE Conference on
Conference_Location
Kobe
ISSN
0191-2216
Print_ISBN
0-7803-3590-2
Type
conf
DOI
10.1109/CDC.1996.574355
Filename
574355
Link To Document