Design of the scaling-wavelet neural network using genetic algorithm

Author

Kim, Seong-Joo ; Kim, Yong-Taek ; Seo, Jae-Yong ; Jeon, Hong-Tae

Author_Institution

Sch. of Electr. & Electron. Eng., Chung-Ang Univ., South Korea

Volume

3

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

2174

Lastpage

2179

Abstract

We propose the composition method of the activation function in the hidden layer with the scaling function which can represent the region where the several wavelet functions can be represented. In this method, we can decrease the size of the network with a few wavelet functions. In addition, when we determine the parameters of the scaling function we can process a rough approximation and then the network becomes more stable. The other wavelets can be determined by the global solution, the genetic algorithm which is suitable for the suggested problem is given, and also, we use the back-propagation algorithm in the learning of the weights. In this step, we approximate the target function with a fine tuning level

Keywords

backpropagation; function approximation; genetic algorithms; neural nets; wavelet transforms; activation function; backpropagation algorithm; complex neural network; composition method; genetic algorithm; global solution; hidden layer; rough approximation; scaling function; scaling-wavelet neural network; Algorithm design and analysis; Educational technology; Function approximation; Genetic algorithms; H infinity control; Interference; Multiresolution analysis; Neural networks; Radial basis function networks; Wavelet analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on

Conference_Location

Honolulu, HI

ISSN

1098-7576

Print_ISBN

0-7803-7278-6

Type

conf

DOI

10.1109/IJCNN.2002.1007478

Filename

1007478