EA crossover schemes for a MLP channel equaliser

Author

Power, P. ; Sweeney, F. ; Cowan, C.F.N.

Author_Institution

Dept. of Electr. & Electron. Eng., Queen´´s Univ., Belfast, UK

Volume

1

fYear

1999

fDate

1999

Firstpage

407

Abstract

This paper presents an evolutionary algorithm (EA) in a form similar to the LMS algorithm, which is applied to MLP learning. The gradient-based update term of the LMS is replaced with the EA non-gradient-based random distancing matrix. This matrix is created to share solution gene information between selected parent chromosomes. The channel equalisation problem is used to compare this algorithm against an EA averaging style operator, which has previously been examined in this area. It is shown that there is an improved learning capability in the MLP filter when the EA updating operators are unrestrained in the range of the gene exchange

Keywords

equalisers; evolutionary computation; learning (artificial intelligence); multilayer perceptrons; EA crossover schemes; MLP channel equaliser; averaging style operator; channel equalisation problem; evolutionary algorithm; gene exchange; gradient-based update term; learning capability; multilayer perceptrons; nongradient-based random distancing matrix; parent chromosomes; solution gene information; Backpropagation algorithms; Evolutionary computation; Filtering; Finite impulse response filter; Iterative algorithms; Least squares approximation; Neural networks; Noise level; Nonlinear filters; Signal to noise ratio;

fLanguage

English

Publisher

ieee

Conference_Titel

Electronics, Circuits and Systems, 1999. Proceedings of ICECS '99. The 6th IEEE International Conference on

Conference_Location

Pafos

Print_ISBN

0-7803-5682-9

Type

conf

DOI

10.1109/ICECS.1999.812309

Filename

812309