Mariana Todorova
ABSTRACT
The problem solved in this paper is the application of a genetic algorithm for identification of linear distributed parameter systems (DPS). A m - function is created in MATLAB for a parameter identification of systems, described by second - order partial differential equations. Numerical examples are given to demonstrate genetic algorithm based identification of DPS by finite difference technique. Both the effects of the number of iterations as well as the effect of the level of a measurement error are examined.
- {1} Boyanov B., H. Hristov, Application of genetic algorithm for fresnel zone plate optimization, JBMSAEM'98, Sep. 1998.Google Scholar
- {2} Coca D., S. Billings, Direct parameter identification of distributed parameter systems, int. J. Systems Sci., voL 30, No 1, pp 11 - 17, 2000.Google Scholar
- {3} Foundations of Genetic Algorithms 2, Edited by L.Darrel Whittley, Morgan Kaufmann Publishers, San Mateo, CA, 1993.Google Scholar
- {4} Genov D., M. Todorova, Identification of distributed parameter systems via Haar's wavelets, International Conference on Automation and Informatics '2000. Google ScholarDigital Library
- {5} Gonzalez-Garcia R., R. Rico-Martinez, G. Kevrekidis, Identification of distributed parameter systems: A neural net based approach, Computers chem. Engng., voL 22, suppl, pp S965 - S968, 1998.Google Scholar
- {6} Mohan B. M., K. B. Datta, Linear time - Invariant distributed parameter system identification via orthogonal functions, Automatica, vol. 27, No 2, pp 409 - 412, 1991. Google ScholarDigital Library
- {7} Tang K. S., K.F. Man, S. Kwong and Q. He, Genetic algorithms and their applications, IEEE Signal Processing Magazine, vol. 13, No. 6, pp. 22-37, Nov. 1996.Google Scholar
Index Terms
- Genetic algorithm based identification of linear distributed parameter systems by finite difference technique
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