DocumentCode
3500530
Title
Optimization of fixed Wavelet Neural Networks
Author
Cordova, J.J. ; Yu, Wen
Author_Institution
Dept. de Control Automatico, CINVESTAV-IPN, Mexico City, Mexico
fYear
2011
fDate
July 31 2011-Aug. 5 2011
Firstpage
3003
Lastpage
3007
Abstract
In the construction of a Wavelet Neural Network, the number of neurons is determined by the traslation coefficient and by the dilations coefficient. Exists two ways to set the value of the traslation coefficients and dilation, one is considering the coefficients like a hidden layer of the network and the other way is establishing fixed values to those coefficients, where there remains the problem of establishing the number of fixed values to be taken, in this paper we present an algorithm to determine the number of fixed values, that they minimize a rate that depends on the approximation error and the number of neurons that are used.
Keywords
approximation theory; function approximation; neural nets; optimisation; wavelet transforms; approximation error; dilation coefficient; fixed wavelet neural network optimization; network hidden layer; traslation coefficient; Approximation algorithms; Approximation methods; Arrays; Biological neural networks; Neurons; Wavelet transforms;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks (IJCNN), The 2011 International Joint Conference on
Conference_Location
San Jose, CA
ISSN
2161-4393
Print_ISBN
978-1-4244-9635-8
Type
conf
DOI
10.1109/IJCNN.2011.6033616
Filename
6033616
Link To Document