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
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;
Conference_Titel :
Decision and Control, 1996., Proceedings of the 35th IEEE Conference on
Conference_Location :
Kobe
Print_ISBN :
0-7803-3590-2
DOI :
10.1109/CDC.1996.574355
