DocumentCode
1087474
Title
Nonlinear parameter estimation via the genetic algorithm
Author
Yao, Leehter ; Sethares, William A.
Author_Institution
Dept. of Electr. Eng., Nat. Taiwan Inst. of Technol., Taipei, Taiwan
Volume
42
Issue
4
fYear
1994
fDate
4/1/1994 12:00:00 AM
Firstpage
927
Lastpage
935
Abstract
A modified genetic algorithm is used to solve the parameter identification problem for linear and nonlinear IIR digital filters. Under suitable hypotheses, the estimation error is shown to converge in probability to zero. The scheme is also applied to feedforward and recurrent neural networks
Keywords
digital filters; feedforward neural nets; filtering and prediction theory; genetic algorithms; parameter estimation; recurrent neural nets; IIR filters; estimation error convergence; feedforward neural networks; genetic algorithm; linear digital filters; nonlinear digital filters; nonlinear parameter estimation; parameter identification; probability; recurrent neural networks; Biological cells; Digital filters; Estimation error; Evolution (biology); Genetic algorithms; Minimization methods; Parameter estimation; Pediatrics; Recurrent neural networks; Surface fitting;
fLanguage
English
Journal_Title
Signal Processing, IEEE Transactions on
Publisher
ieee
ISSN
1053-587X
Type
jour
DOI
10.1109/78.285655
Filename
285655
Link To Document