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Article:Efficient System Identification using a Low Complexity Nonlinear Network with Differential Evolution and its variant based Training Schemes

by H. Pal Thethi, Babita Majhi, G. Panda
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 31 - Number 8
Year of Publication: 2011
Authors: H. Pal Thethi, Babita Majhi, G. Panda
10.5120/3848-5350

H. Pal Thethi, Babita Majhi, G. Panda . Article:Efficient System Identification using a Low Complexity Nonlinear Network with Differential Evolution and its variant based Training Schemes. International Journal of Computer Applications. 31, 8 ( October 2011), 38-46. DOI=10.5120/3848-5350

@article{ 10.5120/3848-5350,
author = { H. Pal Thethi, Babita Majhi, G. Panda },
title = { Article:Efficient System Identification using a Low Complexity Nonlinear Network with Differential Evolution and its variant based Training Schemes },
journal = { International Journal of Computer Applications },
issue_date = { October 2011 },
volume = { 31 },
number = { 8 },
month = { October },
year = { 2011 },
issn = { 0975-8887 },
pages = { 38-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume31/number8/3848-5350/ },
doi = { 10.5120/3848-5350 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:17:38.097622+05:30
%A H. Pal Thethi
%A Babita Majhi
%A G. Panda
%T Article:Efficient System Identification using a Low Complexity Nonlinear Network with Differential Evolution and its variant based Training Schemes
%J International Journal of Computer Applications
%@ 0975-8887
%V 31
%N 8
%P 38-46
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Direct modeling plays a very important role in many engineering applications including telecommunication, power system, image processing, VLSI design, biological processes, control engineering and geophysics applications. In case of control and telecommunication applications, direct modeling is used for channel estimation, parameter estimation and forecasting. There are standard algorithms and models which can be conveniently used for effectively identifying the parameters of simple direct and inverse systems. However, in practice we encounter with various complex systems, whose direct models needs to be created for various applications. As an illustration, the system can be non linear, dynamic or both of it. In such situations, creation of direct models is a difficult task. It is evident from the literature survey that, many sincere attempts have been made to create direct model of such complex systems. However, their performance has been observed to be unsatisfactory. Therefore in the present work, a sincere attempt has been made to address all these issues and provide possible satisfactory solutions by using low complexity nonlinear network and population based differential evolution(DE) based learning algorithm.

References
  1. Y. Xie, B. Guo, L. Xu, J. Li, P. Stoica, “Multistatic adaptive microwave imaging for early breast cancer detection”, IEEE Trans. Biomed. Eng. 53 (8) (2006) 1647–1657.
  2. S. Chen, S.A. Billings, “Representation of non-linear systems: the NARMAX model”, Int. J. Control 49 (1989) 1013–1032
  3. .M. Adjrad, A. Belouchrani, “Estimation of multi component polynomial phase signals impinging on a multisensor array using state-space modeling”, IEEE Trans. Signal Process. 55 (1) (2007) 32–45.
  4. H. Hujiberts, H. Nijmeijer, R. Willems, “System identification in communication with chaotic systems”, IEEE Trans. Circuits Syst. I 47 (6) (2000) 800–808.
  5. F. Dinga, T. Chenb, “Identification of Hammerstein nonlinear ARMAX systems”, Automatica 41 (2005) 1479–1489.
  6. M. Schetzmen, “The Voltera and Winner Theories on Nonlinear Systems”, Wiley, New York, 1980.
  7. E. Hernandez, Y. Arkun, “Control of nonlinear systems using polynomial ARMA models”, AICHE J. 39 (3) (1993) 446–460.
  8. T.T. Lee, J.T. Jeng, “The Chebyshev polynomial-based unified model neural networks for functional approximation”, IEEE Trans. Syst. Man Cybern. B 28 (1998) 925–935.
  9. Narendra, K. S., & Parthasarathy, K. “Identification and control of dynamical systems using neural networks”, IEEE Transactions on Neural Networks, vol. 1 no. 1, pp. 4–27, Mar. 1990.
  10. Nguyen, D. H., & Widrow, B. “Neural networks for self-learning control system.” International Journal of Control, vol. 54, no. 6, pp. 1439–1451, 1991.
  11. M. Srinivas, L. M. Patnaik, “Genetic Algorithm: A Survey’’, IEEE computer, vol. 27, no. 6, pp. 17-26, 1994.
  12. D. B. Fogel, “An Introduction to Simulated Evolutionary Optimization”, IEEE Trans. On SMC., vol. 24, no. 1, pp.
  13. W. Atmar, “Notes on the Simulation of Evolution”, IEEE Trans. On SMC., vol. 24, no. 1, pp. 130-147, 1994.
  14. R. Storn and K. Price, “Differential evolution – A simple and efficient heuristic for global optimization over continuous spaces”, Journal of Global Optimization, vol.11, pp.341-359, 1997.
  15. Vavak, F., Fogarty, T. and Jukes, K “A genetic algorithm with variable range of local search for tracking changing environments”. In Proceedings of the 4th Conference on Parallel Problem Solving from Nature, 1996.
  16. K. Price, R. M. Storn, and J. A. Lampinen, “Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)”, 1st ed. New York: Springer, 2005, ISBN: 3540209506.
  17. J. Vesterstroem and R. Thomsen, “A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems,” Proc. Congr. Evol. Comput., vol. 2, pp. 1980–1987, 2004.
  18. J. Andre, P. Siarry, and T. Dognon, “An improvement of the standard genetic algorithm fighting premature convergence in continuous optimization,” Advance in Engineering Software 32, pp. 49–60, 2001.
  19. O. Hrstka and A. Ku˘cerová, “Improvement of real coded genetic algorithm based on differential operators preventing premature convergence,” Advance in Engineering Software 35, pp. 237–246, 2004.
  20. R.Storn, “Differential evolution design for an IIR-filter with requirements for magnitude and group delay”. Technical Report TR-95-026, International Computer Science Institute, Berkeley, CA 1995.
  21. Qing, A.: “Electromagnetic inverse scattering of multiple perfectly conducting cylinders by differential evolution strategy with individuals in groups (GDES)”. IEEE Trans. Antennas Propagate. 52(5), 1223–1229 (2004).
  22. Lakshminarasimman, L., Subramanian, S.: “Hydrothermal optimal power flow using modified hybrid differential evolution”. Caledonian J. Engg. 3(1), 8–14 (2007a)
  23. Lakshminarasimman, L., Subramanian, S.: “Short-term scheduling of hydrothermal power system with cascaded reservoirs by using modified differential evolution”. IEE Proc. Gener. Transm. Distrib. 153(6), 693–700 (2006)
  24. Yang, S., Gan, Y.B., Qing, A, “ Sideband suppression in time-modulated linear arrays by the differential evolution algorithm,” IEEE Antennas Wireless Propagate. Lett. 1, pp. 173–175, 2002.
  25. Y.H. Pao, S. M. Phillips and D. J. Sobajic, “Neural-net computing and intelligent control systems.” Int. J. Conr. , vol. 56, no.2, pp.263-289, 1992.
  26. Patra, J. C., Pal, R. N., Chatterji, B. N., & Panda, G. “Identification of nonlinear dynamic systems using functional link artificial neural networks”, IEEE Transactions in Systems Man and Cybernetics-Part B: Cybernetics, vol.29 no. 2,pp. 254–262, 1999.
Index Terms

Computer Science
Information Sciences

Keywords

System identification for dynamic systems FLANN DE

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