This paper describes the application of genetic algorithms (GA) to identify a class of nonlinear SISO models composed of a memoryless nonlinearity in series with a linear transfer function. In contrast with recent literature on the considered problem, we encode in the chromosomes also the structure of the model (type of nonlinearity, number of zeros and poles), and use the GA to identify both the optimal structure and the associated parameters. New operators for mutation and crossover to deal with chromosomes with variable length are introduced. The effectiveness of the approach is tested on a set of case studies derived from literature

Genetic identification of dynamical systems with static nonlinearities

DOTOLI, Mariagrazia;MAIONE, Guido;NASO, David;TURCHIANO, Biagio
2001

Abstract

This paper describes the application of genetic algorithms (GA) to identify a class of nonlinear SISO models composed of a memoryless nonlinearity in series with a linear transfer function. In contrast with recent literature on the considered problem, we encode in the chromosomes also the structure of the model (type of nonlinearity, number of zeros and poles), and use the GA to identify both the optimal structure and the associated parameters. New operators for mutation and crossover to deal with chromosomes with variable length are introduced. The effectiveness of the approach is tested on a set of case studies derived from literature
IEEE SMCia/01, Mountain Workshop on Soft Computing in Industrial Applications
0-7803-7154-2
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11589/13766
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • Scopus 21
  • ???jsp.display-item.citation.isi??? 11
social impact