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
fDate :
4/1/1994 12:00:00 AM
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;
Journal_Title :
Signal Processing, IEEE Transactions on
