Skip to main content
SpringerLink
Log in
Book cover

Handbook of Swarm Intelligence pp 469–485Cite as

Direct and Inverse Modeling of Plants Using Cat Swarm Optimization

  • Chapter

Part of the book series: Adaptation, Learning, and Optimization ((ALO,volume 8))

Abstract

Derivative based learning rule poses stability problem when used in adaptive plant modeling. In addition the performance of these techniques deteriorates when used for non-linear plant modeling. In this chapter, the plant modeling task is formulated as an optimization problem. A recently introduced evolutionary algorithm, cat swarm optimization (CSO), is used to develop a new population based learning rule for the model. Adaptive modeling of a benchmarked plant is carried out through simulation study. The performance of the CSO in presence of nonlinearity in the plant is also studied. The results demonstrate superior performance of the CSO compared to that achieved by genetic algorithm (GA) and particle swarm optimization (PSO) based approaches for adaptive modeling.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Narendra, K.S., Parthasarathy, K.: Identification and control of dynamical systems using neural networks. IEEE Trans. Neural Networks 1, 4–26 (1990)

    Article  Google Scholar 

  2. Patra, J.C., Kot, A.C., Panda, G.: An intelligent pressure sensor using neural networks. IEEE Trans. Instrumentation and Measurement 49, 829–834 (2000)

    Article  Google Scholar 

  3. Pachter, M., Reynolds, O.R.: Identification of a discrete time dynamical system. IEEE Trans. Aerospace Electronic System 36, 212–225 (2000)

    Article  Google Scholar 

  4. Giannakis, G.B., Serpedin, E.: A bibliography on nonlinear system identification. Signal Processing 83(3), 533–580 (2001)

    Article  Google Scholar 

  5. Robinson, E.A., Durrani, T.: Geophysical Signal Processing. Prentice-Hall, Englewood Cliffs (1986)

    Google Scholar 

  6. Das, D.P., Panda, G.: Active mitigation of nonlinear noise processes using a novel filtered-s lms algorithm. IEEE Trans. Speech and Audio Processing 12, 313–322 (2004)

    Article  Google Scholar 

  7. Widrow, B., Strearns, S.D.: Adaptive Signal Processing. Prentice-Hall, Englewood Cliffs (1985)

    MATH  Google Scholar 

  8. Gibson, G.J., Siu, S., Cowan, C.F.N.: The application of nonlinear structures to the reconstruction of binary signals. IEEE Trans. signal processing 39(8), 1877–1884 (1991)

    Article  Google Scholar 

  9. Lucky, R.W.: Techniques for adaptive equalization of digital communication systems. Bell Sys. Tech. J. 45, 255–286 (1966)

    Google Scholar 

  10. Sun, H., Mathew, G., Farhang-Boroujeny, B.: Detection techniques for high density magnetic recording. IEEE Trans. Magnetics 41(3), 1193–1199 (2005)

    Article  Google Scholar 

  11. Griffiths, L.J., Smolka, F.R., Trenbly, L.D.: Adaptive deconvolution: a new technique for processing time varying seismic data. Geophysics (1977)

    Google Scholar 

  12. Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Heidelberg (2003)

    MATH  Google Scholar 

  13. Engelbrecht, A.: Computational Intelligence: An introduction. Wiley & Sons, Chichester (2002)

    Google Scholar 

  14. Goldberg, D.E.: Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading (1989)

    MATH  Google Scholar 

  15. Kennedy, J., Eberhart, R.C., Shi, Y.: Swarm intelligence. Morgan Kaufmann Publishers, San Francisco (2001)

    Google Scholar 

  16. Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE control system magazine 22, 52–67 (2002)

    Article  Google Scholar 

  17. Dasgupta, D.: Artificial Immune Systems and their Applications. Springer, Heidelberg (1999)

    MATH  Google Scholar 

  18. Majhi, B., Panda, G., Choubey, A.: Efficient scheme of pole-zero system identification using particle swarm optimization technique. In: IEEE Congress on Evolutionary Computation, pp. 446–451 (2008)

    Google Scholar 

  19. Katari, V., Malireddi, S., Bendapudi, S.K.S., Panda, G.: Adaptive nonlinear system identification using Comprehensive Learning PSO. In: 3rd International Symposium on Communications, Control and Signal Processing, vol. 2008, pp. 434–439 (2008)

    Google Scholar 

  20. Theofilatos, K., Beligiannis, G., Likothanassis, S.: Combining evolutionary and stochastic gradient techniques for system identification. Journal of Computational and Applied Mathematics 227(1), 147–160 (2009)

    Article  MATH  MathSciNet  Google Scholar 

  21. Pao, Y.H.: Adaptive Pattern Recognition and Neural Networks. Addison Wesley, Reading (1989)

    MATH  Google Scholar 

  22. Haykin, S.: Neural Networks: A comprehensive foundation, 2nd edn. Pearson Education Asia (2002)

    Google Scholar 

  23. Chu, S., Tsai, P., Pan, J.: Cat swarm optimization. In: Yang, Q., Webb, G. (eds.) PRICAI 2006. LNCS (LNAI), vol. 4099, pp. 854–858. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  24. Chu, S., Tsai, P.: Computational intelligence based on the behavior of cats. International Journal of Innovative Computing, Information and Control 3(1), 163–173 (2007)

    Google Scholar 

  25. Tsai, P., Pan, J., Chen, S., Liao, B., Hao, S.: Parallel cat swarm optimization. In: Proc. of the Seventh International Conference on Machine Learning and Cybernetics, Kunming, pp. 3328–3333 (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Indian Institute of Technology, Bhubaneswar, India

    Ganapati Panda & Pyari Mohan Pradhan

  2. Institute of Technical Education and Research, Bhubaneswar, India

    Babita Majhi

Authors
  1. Ganapati Panda
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pyari Mohan Pradhan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Babita Majhi
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Electrical Engineering Department, Indian Institute of Technology, Delhi , 110016, New Delhi, India

    Bijaya Ketan Panigrahi

  2. Director of Research and Postgraduate Office, Xi’an Jiaotong-Liverpool University , 215123, Suzhou, China

    Yuhui Shi

  3. School of Electrical & Electronic Engineering, Nanyang Technological University , 639798, Singapore

    Meng-Hiot Lim

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Panda, G., Pradhan, P.M., Majhi, B. (2011). Direct and Inverse Modeling of Plants Using Cat Swarm Optimization. In: Panigrahi, B.K., Shi, Y., Lim, MH. (eds) Handbook of Swarm Intelligence. Adaptation, Learning, and Optimization, vol 8. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17390-5_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-17390-5_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17389-9

  • Online ISBN: 978-3-642-17390-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation