DocumentCode
3342111
Title
Training RBF networks with perturbation methods
Author
Bostel, A.J.
Author_Institution
Dept. of Electron. Syst. Eng., Essex Univ., Colchester, UK
Volume
3
fYear
1995
fDate
30 Apr-3 May 1995
Firstpage
1699
Abstract
This paper describes a gradient descent technique for training radial basis function (RBF) networks which is suitable for hardware implementation. The method dynamically adjusts the positions and the widths of the basis functions so as to reduce the total output error of the network while the output connection weights are being trained. The algorithm is demonstrated by using it to train an RBF network to perform simple logical functions
Keywords
functions; learning (artificial intelligence); neural nets; perturbation techniques; RBF networks; dynamic adjustment; gradient descent technique; network training; output connection weights; perturbation method; radial basis function; supervised learning; total output error reduction; Clustering algorithms; Equations; Function approximation; Hardware; Neural networks; Perturbation methods; Radial basis function networks; Systems engineering and theory; Training data; Very large scale integration;
fLanguage
English
Publisher
ieee
Conference_Titel
Circuits and Systems, 1995. ISCAS '95., 1995 IEEE International Symposium on
Conference_Location
Seattle, WA
Print_ISBN
0-7803-2570-2
Type
conf
DOI
10.1109/ISCAS.1995.523739
Filename
523739
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