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Estimation of Parameters for Model Matching using Genetic Algorithms

by Sheeba Ps
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 108 - Number 10
Year of Publication: 2014
Authors: Sheeba Ps
10.5120/18944-9954

Sheeba Ps . Estimation of Parameters for Model Matching using Genetic Algorithms. International Journal of Computer Applications. 108, 10 ( December 2014), 1-6. DOI=10.5120/18944-9954

@article{ 10.5120/18944-9954,
author = { Sheeba Ps },
title = { Estimation of Parameters for Model Matching using Genetic Algorithms },
journal = { International Journal of Computer Applications },
issue_date = { December 2014 },
volume = { 108 },
number = { 10 },
month = { December },
year = { 2014 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume108/number10/18944-9954/ },
doi = { 10.5120/18944-9954 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:42:35.631562+05:30
%A Sheeba Ps
%T Estimation of Parameters for Model Matching using Genetic Algorithms
%J International Journal of Computer Applications
%@ 0975-8887
%V 108
%N 10
%P 1-6
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper develops an optimization procedure to estimate the parameter values of a given dynamic system. The actual nonlinear model of the system is assumed available with the approximate range of parameter values. The model used in this work is the PS4 actuator with nozzle dynamics which is used for the liquid upper stage control system of PSLV. The objective is to estimate the parameter values so that the error between actual system output and the simulated output is minimized. This is achieved through Genetic Algorithms(GA) which is a global optimization technique. GAs are stochastic algorithms based on Darwin's theory of survival of the fittest. They are inspired by biological phenomena of natural genetics and natural selection. The basic elements of natural genetics- reproduction, crossover and mutation- are used in the genetic search procedures. GA is proved robust and efficient in finding optimal solutions in complex problem spaces.

References
  1. Goldberg D. E. , Genetic Algorithms in search, optimization and machine learning, Addison-Wesely, 1989.
  2. S. S. Rao, Engineering Optimization, Theory and Practice, New Age International (P) Ltd, 1998.
  3. ohn. J. Grefenstette, "Optimization of control parameters for genetic algorithms", IEEE Transactions on systems, man and cybernetics, Vol. 16, No. 1, pp. 122-128, Jan 1986.
  4. Raif Saloman, "Evolutionary algorithms and gradient search: Similarities and differences", IEEE Transactions on Evolutionary computation, Vol. 2, No. 2, pp. 45-55, July 1998.
  5. K. F Man, K. S. Tang, S. K. Wong and W. A. Halang, "Genetic Algorithms for control and signal processing, Springer- Verlag, London Ltd, 1997.
  6. L. Yao andW. A. Sethares, Nonlinear parameter estimation via Genetic Algorithm, IEEE Transactions on signal processing, Vol. 42, pp. 927-935, April 1994.
  7. Carlos M. Fnseca, Multiobjective optimization and multiple constraint handling with evolutionary algorithms Part I: A unified formulation, IEEE Transactions on System, man and Cybernetics- Part A, systems , humans, Vol. 28, No. 1, pp. 26- 37, Jan 1998.
  8. Soderstrom and Stoica, System Identification, Prentice Hall International Ltd, 1989.
  9. Kristinsson, K. , System Identification and Control using Genetic Algorithms, Systems, Man and Cybernetics,, Vol. 22, Issue 5, pp. 1033-1046, August 2002.
  10. Alonge, F. , D'Ippolito, F. , Ferrante, G. , Raimondi, F. M. , Parameter identification of induction motor model using genetic algorithms, IEE Proceedings of Control Theory and Applications, Vol. 145, Issue 6, Nov 1998.
  11. Nur Hayati Kasim, Hairudin Abdul Majid, Azurah A. Samah, Parameter estimation of warranty cost model using genetic algorithm, International Journal of Soft Computing and Engineering , Vol. 2, Issue 5,pp. 2231-2307, November 2012.
  12. M. Zagroubaa,A. Sellamia,M. Bouacha, Identification of PV solar cells and modules parameters using the genetic algorithms: Application to maximum power extraction, Solar Energy, Vol. 84, Issue 5, pp. 860866, May 2010.
Index Terms

Computer Science
Information Sciences

Keywords

Genetic Algorithm Optimization Parameter Estimation

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