Closed-loop Identification of Hammerstein Systems Using Hybrid Neural Model Identified by Genetic Algorithms

Author

Vall, O. M Mohamed ; Radhi, M.

Author_Institution

Dept. Genie Electrique, Ecole Nationale d´´Ingenieurs de Tunis

Volume

2

fYear

2005

fDate

28-30 Nov. 2005

Firstpage

1027

Lastpage

1030

Abstract

In this paper we present an approach for the closed loop identification of Hammerstein systems. In this approach we propose modelling the system to be identified by a hybrid neural model, which is composed of a neural network (NN), connected in series with a linear model. To optimize the proposed model, genetic algorithms are used. The system to be identified is in closed-loop with variable structure controller (CSV) in order to have a command signal rich in commutations and consequently a good identification. A simulation example is given in order to show the effectiveness of the proposed approach

Keywords

closed loop systems; genetic algorithms; neural nets; nonlinear control systems; Hammerstein system; closed-loop identification; genetic algorithm; hybrid neural model; variable structure controller; Control systems; Distillation equipment; Fuzzy logic; Genetic algorithms; Los Angeles Council; Neural networks; Open loop systems; Output feedback; Production; Signal processing; Closed loop identification; Genetic Algorithms; Hammerstein system; Neural Network; Variable Structure Controller.;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence for Modelling, Control and Automation, 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, International Conference on

Conference_Location

Vienna

Print_ISBN

0-7695-2504-0

Type

conf

DOI

10.1109/CIMCA.2005.1631604

Filename

1631604