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
fDate :
29 June-1 July 1994
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;
Conference_Titel :
American Control Conference, 1994
Print_ISBN :
0-7803-1783-1
DOI :
10.1109/ACC.1994.752283
