Skip to main content

Optimization problems on concurrent testing solved by neural networks

  • Applications
  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 540))

Abstract

This communication presents an Extended Hopfield Neural Network which has been applied to design the extra circuitry for testing a digital circuit during its normal operation, problem which we have shown to be equivalent to the problem of selecting an optimal set of Reed-Muller spectral coefficients. It has been suggested that neural networks, in particular the Hopfield Neural network, may be used to solve linear programming problems. Here, we show how a modification of the Hopfield Network structure also allows to solve non-linear programming problems.

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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

VII. References

  1. Garey, M.R.; Johnson, D.S.:"Computers and Intractability: A Guide to the Theory of NP-Completeness". Freeman, San Francisco, 1979.

    Google Scholar 

  2. Hopfield, J.J.:"Neural Networks and physical systems with emergent collective computational abilities". Proc. of the National Academy of Sciences, USA 79, pp.2554–2558. 1982.

    Google Scholar 

  3. Hopfield, J.J.; Tank, D.W.:"Neural Computation of Decisions in Optimization Problems". Biol. Cybern. 52, 141, 1985.

    Google Scholar 

  4. Müller, B.; Reinhart, J.:"Neural Networks. An Introduction". Springer-Verlag, Berlin Heidelberg, pp.104–112. 1990.

    Google Scholar 

  5. Lee, B.W.; Sheu, B.J.:"Modified Hopfield Neural Networks for Retrieving the Optimal Solution". IEEE Trans. on Neural Networks, Vol.2, No.1, pp.137–142. January, 1991.

    Google Scholar 

  6. Lee, B.W.; Sheu, B.J.:"Hardware Annealing in Electronic Neural Networks". IEEE Trans. on Circuits and Systems, Vol.38, No.1, pp.134–137. January, 1991.

    Google Scholar 

  7. Peretto, P.:"Discrete Linear Programming Problems solved by Digital Neural Networks". Journées D'Électronique, pp.117–130, Lausanne, 10–12 october, 1989.

    Google Scholar 

  8. Lala, P.K.:"Fault Tolerant & Fault Testable Hardware Design". Prentice Hall Int., 1985.

    Google Scholar 

  9. Fujiwara, E.; Matsuoka, K.: "A Self-Checking Generalized Prediction Checker and Its Use for Built-In Testing". IEEE Transactions on Computers, Vol. C-36, No. 1, pp. 86–93, January 1987.

    Google Scholar 

  10. Ortega, J.; Lloris, A.; Prieto,A.:"Aplicación de una nueva transformada a la comprobación de circuitos digitales". Monografías del Departamento de Electrónica, Univ. de Granada, no. 17, ISBN 84-600-7580-X. 1990.

    Google Scholar 

  11. Damarla, T.R.; Karpovsky, M.:"Fault Detection in Combinational Networks by Reed-Muller Transforms". IEEE Trans. on Comp., vol.38, No.6, pp.788–797. June, 1989.

    Google Scholar 

  12. Pradhan, D.K.; Sandeep, K.G.; Karpovsky, M.G.:"Aliasing Probability for Multiple Input Signature Analyzer". IEEE Trans. on Comp., Vol.39, No.4, pp.586–591. April, 1990.

    Google Scholar 

  13. Pelayo, F.J.; Prieto, A.; Pino, B.; Ortega, J.; Martin-Smith, P.:"A new computational element for neural networks". Proceedings of the 4th International Symposium on Knowledge Engineering. Barcelona (Spain). pp.247–250. May 9–11, 1990.

    Google Scholar 

  14. Pelayo, F.J.; Prieto, A.; Pino, B.; Ortega, J.; Martin-Smith, P.:"Hardware Implementation of a neuron model". Proceedings of the 8th International Symposium Applied Informatics, IASTED. Insbruck (Austria). Acta-Press (USA), pp.262–264. February 20–23, 1990.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Alberto Prieto

Rights and permissions

Reprints and permissions

Copyright information

© 1991 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ortega, J., Prieto, A., Pelayo, F.J., Lloris, A., Martin-Smith, P. (1991). Optimization problems on concurrent testing solved by neural networks. In: Prieto, A. (eds) Artificial Neural Networks. IWANN 1991. Lecture Notes in Computer Science, vol 540. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0035918

Download citation

  • DOI: https://doi.org/10.1007/BFb0035918

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-54537-8

  • Online ISBN: 978-3-540-38460-1

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics