Advertisement

Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Associative memory approach to the identification of structural and mechanical systems

Abstract

This paper presents a new method for identification of parameters in nonlinear structural and mechanical systems in which the initial guesses of the unknown parameter vectors may be far from their true values. The method uses notions from the field of artificial neural nets and, using an initial set of training parameter vectors, generates in an adaptive fashion other relevant training vectors to yield identification of the parameter vector in a recursive fashion. The simplicity and power of the technique are illustrated by considering three highly nonlinear systems. It is shown that the technique presented here yields excellent estimates with only a limited amount of response data, even when each element of the set comprising the initial training parameter vectors is far from its true value—in fact, sufficiently far that the usual recursive identification schemes fail to converge.

This is a preview of subscription content, log in to check access.

References

  1. 1.

    Rodriguez, G., Editor,Proceedings of the Workshop on Identification and Control of Flexible Structures, Vols. 1 and 2, Publication 85-29, Jet Propulsion Laboratory, 1985.

  2. 2.

    Kalaba, R. E., andSpringarn, K.,Control, Identification, and Input Optimization, Plenum, New York, New York, 1982.

  3. 3.

    Ljung, L.,System Identification: Theory for the User, McGraw Hill, New York, New York, 1988.

  4. 4.

    Ljung, L., andSoderstrom, T.,Theory and Practice of Recursive Identification, MIT Press, Cambridge, Massachusetts, 1983.

  5. 5.

    Udwadia, F. E., Garba, J., andGhodsi, A.,Parameter Identification Problems in Structural and Geotechnical Engineering, Journal of Engineering Mechanics, Vol. 110, pp. 1409–1432, 1984.

  6. 6.

    Udwadia, F. E., andSharma, D. K.,Some Uniqueness Problems in the Identification of Building Structural Systems, SIAM Journal on Applied Mathematics, Vol. 34, pp. 104–118, 1978.

  7. 7.

    Kosko, B.,Bidirectional Associative Memories, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 18, pp. 49–60, 1988.

  8. 8.

    Kohonen, T.,Self-Organization and Associative Memory, Springer-Verlag, New York, New York, 1988.

  9. 9.

    Rumelhart, D., andMcClelland, J., Editors,Parallel Distributed Processing: Exploration in the Microstructure of Cognition, Vols. 1 and 2, MIT Press, Cambridge, Massachusetts, 1988.

  10. 10.

    Rehak, D., Thewalt, C. R., andDoo, L. B.,Neutral Network Approaches in Structural Mechanics Confrontations, Proceedings of ASCE Structures Congress, pp. 168–176, 1989.

  11. 11.

    Kalaba, R. E., andUdwadia, F. E.,An Adaptive Learning Approach to the Identification of Structural and Mechanical Systems, International Journal of Computers and Mathematics with Applications, Vol. 22, pp. 67–75, 1990.

Download references

Author information

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Kalaba, R.E., Udwadia, F.E. Associative memory approach to the identification of structural and mechanical systems. J Optim Theory Appl 76, 207–223 (1993). https://doi.org/10.1007/BF00939605

Download citation

Key Words

  • Parameter identification
  • nonlinear mechanical and structural systems
  • associative memory
  • adaptive training
  • recursive memory matrix