DocumentCode
335375
Title
Using genetic algorithms to estimate the optimum width parameter in radial basis function networks
Author
Kuo, L.E. ; Melsheimer, S.S.
Author_Institution
Dept. of Chem. Eng., Clemson Univ., SC, USA
Volume
2
fYear
1994
fDate
29 June-1 July 1994
Firstpage
1368
Abstract
Radial basis function (RBF) networks are an attractive tool for modeling dynamic systems for control purposes. This paper presents a new methodology to find the optimum width parameters in the RBF network model. This methodology, which combines genetic algorithms and the orthogonal least squares method, is described in detail. Finally, two examples illustrate the usefulness of this method.
Keywords
feedforward neural nets; genetic algorithms; least squares approximations; parameter estimation; dynamic systems; genetic algorithms; optimum width parameter; orthogonal least squares method; radial basis function networks; Chemical engineering; Control system synthesis; Euclidean distance; Feedforward neural networks; Genetic algorithms; Intelligent networks; Learning systems; Neural networks; Radial basis function networks; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
American Control Conference, 1994
Print_ISBN
0-7803-1783-1
Type
conf
DOI
10.1109/ACC.1994.752283
Filename
752283
Link To Document