Title :
Migration-based multiobjective genetic programming for nonlinear system identification
Author :
Ferariu, L. ; Patelli, A.
Author_Institution :
Dept. of Autom. Control & Appl. Inf., Gh. Asachi Tech. Univ., Iasi, Romania
Abstract :
Nonlinear system identification is addressed by means of genetic programming. For a flexible selection of model structure and parameters, a multiobjective optimization of the tree encoded individuals is carried out, in terms of accuracy and parsimony. The paper suggests a new optimization algorithm based on the evolvement of two quasi-independent subpopulations, which makes use of a flexible migration scheme with adaptive thresholds and multiple rates. By efficiently exploiting the concept of dominance analysis, the algorithm is able to select compact and accurate models, with good generalization capabilities. The approach is compliant with nonlinear models, linear in parameters. That permits the hybridization with QR decomposition and the use of enhanced genetic operators, aimed to increase the algorithm convergence speed. The performances of the suggested design procedure are illustrated by the identification of two nonlinear industrial subsystems.
Keywords :
genetic algorithms; identification; nonlinear control systems; trees (mathematics); QR decomposition; adaptive threshold; convergence speed; dominance analysis; flexible model structure selection; migration-based multiobjective genetic programming; nonlinear system identification; optimization algorithm; quasi independent subpopulation; tree encoding; Automatic programming; Computational intelligence; Computational modeling; Design optimization; Electrical equipment industry; Evolutionary computation; Genetic programming; Informatics; Nonlinear systems; Performance analysis;
Conference_Titel :
Applied Computational Intelligence and Informatics, 2009. SACI '09. 5th International Symposium on
Conference_Location :
Timisoara
Print_ISBN :
978-1-4244-4477-9
Electronic_ISBN :
978-1-4244-4478-6
DOI :
10.1109/SACI.2009.5136295