A hybrid neural-genetic multimodel parameter estimation algorithm

Author

Petridis, Vassilios ; Paterakis, Emmanuel ; Kehagias, Athanasios

Author_Institution

Dept. of Electr. & Comput. Eng., Aristotelian Univ. of Thessaloniki, Greece

Volume

9

Issue

5

fYear

1998

fDate

9/1/1998 12:00:00 AM

Firstpage

862

Lastpage

876

Abstract

We introduce a hybrid neural-genetic multimodel parameter estimation algorithm. The algorithm is applied to structured system identification of nonlinear dynamical systems. The main components of the algorithm are: 1) a recurrent incremental credit assignment neural network which computes a credit function for each member of a generation of models; and 2) a genetic algorithm which uses the credit functions as selection probabilities for producing new generations of models. The neural network and genetic algorithm combination is applied to the task of finding the parameter values which minimize the total square output error: the credit function reflects the closeness of each model´s output to the true system output and the genetic algorithm searches the parameter space by a divide-and-conquer technique. The algorithm is evaluated by numerical simulations of parameter estimation for a planar robotic manipulator and a waste water treatment plant

Keywords

divide and conquer methods; genetic algorithms; manipulators; nonlinear dynamical systems; parameter estimation; recurrent neural nets; water treatment; divide-and-conquer technique; genetic algorithm; multimodel parameter estimation; nonlinear dynamical systems; recurrent incremental credit assignment neural net; robotic manipulator; system identification; waste water treatment plant; Computer networks; Genetic algorithms; Manipulators; Neural networks; Nonlinear dynamical systems; Numerical simulation; Orbital robotics; Parameter estimation; Recurrent neural networks; System identification;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/72.712158

Filename

712158