Skip to main content

PSO Assisted NURB Neural Network Identification

  • Conference paper
  • 2501 Accesses

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 7389)

Abstract

A system identification algorithm is introduced for Hammerstein systems that are modelled using a non-uniform rational B-spline (NURB) neural network. The proposed algorithm consists of two successive stages. First the shaping parameters in NURB network are estimated using a particle swarm optimization (PSO) procedure. Then the remaining parameters are estimated by the method of the singular value decomposition (SVD). Numerical examples are utilized to demonstrate the efficacy of the proposed approach.

Keywords

  • B-spline
  • NURB neural networks
  • De Boor algorithm
  • Hammerstein model
  • pole assignment controller
  • particle swarm optimization
  • system identification

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (Canada)
  • Compact, lightweight 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. Bai, E.W., Fu, M.Y.: A Blind Approach to Hammerstein Model Identification. IEEE Transactions on Signal Processing 50(7), 1610–1619 (2002)

    CrossRef  Google Scholar 

  2. Billings, S.A., Fakhouri, S.Y.: Nonlinear System Identification Using the Hammerstein Model. International Journal of Systems Science 10, 567–578 (1979)

    CrossRef  MathSciNet  MATH  Google Scholar 

  3. de Boor: A Practical Guide to Splines. Springer, New York (1978)

    CrossRef  MATH  Google Scholar 

  4. Chaoui, F.Z., Giri, F., Rochdi, Y., Haloua, M., Naitali, A.: System Identification Based Hammerstein Model. International Journal of Control 78(6), 430–442 (2005)

    CrossRef  MathSciNet  MATH  Google Scholar 

  5. Farin, G.: Curves and Surfaces for Comnputer-aided Geometric Design: a Practical Guide. Academic Press, Boston (1994)

    Google Scholar 

  6. Goethals, I., Pelckmans, K., Suykens, J.A.K., Moor, B.D.: Identification of MIMO Hammerstein Models Using Least Squares Support Vector Machines. Automatica 41, 1263–1272 (2005)

    CrossRef  MATH  Google Scholar 

  7. Greblicki, W.: Stochastic Approximation in Nonparametric Identification of Hammerstein Systems. IEEE Transactions on Automatic Control 47(11), 1800–1810 (2002)

    CrossRef  MathSciNet  Google Scholar 

  8. Greblicki, W., Pawlak, M.: Identification of discrete Hammerstein Systems Using Kernel Regression Estimate. IEEE Transactions on Automatic Control AC-31(1), 74–77 (1986)

    CrossRef  Google Scholar 

  9. Guru, S.M., Halgamuge, S.K., Fernando, S.: Particle Swarm Optimisers for Cluster Formation in Wireless Sensor Networks. In: Proc. 2005 Int. Conf. Intelligent Sensors, Sensor Networks and Information Processing, Melbourne, Australia, pp. 319–324 (2005)

    Google Scholar 

  10. Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proc. of 1995 IEEE Int. Conf. Neural Networks, Perth, Australia, vol. 4, pp. 1942–1948 (1995)

    Google Scholar 

  11. Kennedy, J., Eberhart, R.C.: Swarm Intelligence. Morgan Kaufmann (2001)

    Google Scholar 

  12. Lang, Z.Q.: A Nonparametric Polynomial Identification Algorithm for the Hammerstein System. IEEE Transactions on Automatic Control 42, 1435–1441 (1997)

    CrossRef  MATH  Google Scholar 

  13. van der Merwe, D.W., Engelbrecht, A.P.: Data Clustering Using Particle Swarm Optimization. In: Proc. CEC 2003, Cabberra, Australia, pp. 215–220 (2003)

    Google Scholar 

  14. Ratnaweera, A., Halgamuge, S.K., Watson, H.C.: Self-organizing Hierarchical Particle Swarm Optimizer with Time-varying Acceleration Coefficients. IEEE Trans. Evolutionary Computation 8, 240–255 (2004)

    CrossRef  Google Scholar 

  15. Stoica, P., Söderström, T.: Instrumental Variable Methods for Identification of Hammerstein Systems. International Journal of Control 35, 459–476 (1982)

    CrossRef  MathSciNet  MATH  Google Scholar 

  16. Verhaegen, M., Westwick, D.: Identifying Mimo Hammerstein Systems in the Context of Subspace Model Identification. International Journal of Control 63(2), 331–349 (1996)

    CrossRef  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Hong, X., Chen, S. (2012). PSO Assisted NURB Neural Network Identification. In: Huang, DS., Jiang, C., Bevilacqua, V., Figueroa, J.C. (eds) Intelligent Computing Technology. ICIC 2012. Lecture Notes in Computer Science, vol 7389. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31588-6_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-31588-6_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31587-9

  • Online ISBN: 978-3-642-31588-6

  • eBook Packages: Computer ScienceComputer Science (R0)