Advertisement

Dynamic Neural Nets in the State Space Utilized in Non-Linear Process Identification

  • R. C. L. de Oliveira
  • F. M. de Azevedo
  • J. M. Barreto
Conference paper

Abstract

This work shows the use of a novel neural model for identification of non-linear process. The neural model make use of internal dynamic with dynamical neurons. The parameters responsible for the dynamic of the neural net are adjustable, giving a high flexibility for the neural model in process identification.

Keywords

Intermediate Layer Neural Model Hyperbolic Tangent Neuron Layer Specific Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    F.M. de Azevedo. Contribution to the Study of Neural Networks in Dynamical Expert Systems. PhD thesis, Institut D’Informatique, FUNDP, Namur, Belgium, 1993.Google Scholar
  2. [2]
    R.C.L. de Oliveira, C.L. Nascimento Jr., and T. Yoneyama. A fault tolerant controller based on neural nets. In Proceedings of the IEE International Conference on Control’91, volume 1, pages 399–404, Endiburgh, UK, 1991.Google Scholar
  3. [3]
    K.J. Hunt, D. Sbarbaro, R. Żbikowski, and P.J. Gawthrop. Neural networks for control systems: a survey. Automatic, 28(6):1083–1122, 1992.MathSciNetMATHCrossRefGoogle Scholar
  4. [4]
    M. Jordan. The Learning of Representations for Sequential Performance. PhD thesis, University of California, San Diego, California, USA, 1989.Google Scholar
  5. [5]
    C.L. Nascimento Jr. Artificial Neural Networks in Control and Optimization. PhD thesis, Control System Centre, Faculty of Technology, University of Manchester, Manchester, UK, 1994.Google Scholar
  6. [6]
    L. Personnaz, I. Guyon, and G. Dreyfus. Information storage and retrieval in spin-glass like neural networks. Journal de Physique, Lettres (Orsay, France), 46:L–359–L–365, 1985.Google Scholar
  7. [7]
    J. Sjoberg. Non-Linear System Identification with Neural Networks. PhD thesis, Department of Electrical Engineering, Linkoping University, Linkoping, Sweden, 1995.Google Scholar
  8. [8]
    R. Williams and D. Zipser. Experimental analysis of the real-time recurrent learning algorithm. Connect. Sci., 1:179–211, 1990.Google Scholar
  9. [9]
    R.W. Żbikowski. Recurrent Neural Networks: Some Control Aspects. PhD thesis, Faculty of Engineering, Glasgow University, Glasgow, UK, 1994.Google Scholar

Copyright information

© Springer-Verlag Wien 1998

Authors and Affiliations

  • R. C. L. de Oliveira
    • 1
  • F. M. de Azevedo
    • 1
  • J. M. Barreto
    • 2
  1. 1.GPEB-Dept. of Electrical EngineeringFederal University of Santa CatarinaBrazil
  2. 2.Dept. of Informatics and StatisticsFederal University of Santa CatarinaBrazil

Personalised recommendations