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Implementation of Two-Stage Hopfield Model and Its Application in Nonlinear Systems

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Artificial Intelligence and Soft Computing - ICAISC 2004 (ICAISC 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3070))

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Abstract

This paper presents an efficient neural network for solving constrained nonlinear optimization problems. More specifically, a two-stage neural network architecture is developed and its internal parameters are computed using the valid-subspace technique. The main advantage of the developed network is that it treats optimization and constraint terms in different stages with no interference with each other. Moreover, the proposed approach does not require specification of penalty or weighting parameters for its initialization.

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References

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Author information

Authors and Affiliations

  1. School of Engineering Department of Electrical Engineering, São Paulo State University – UNESP/FE/DEE, CP 473, CEP 17033.360, Bauru, SP, Brazil

    Ivan Nunes da Silva, Jose Alfredo C. Ulson & Andre Nunes de Souza

Authors
  1. Ivan Nunes da Silva
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  2. Jose Alfredo C. Ulson
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  3. Andre Nunes de Souza
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Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Academy of Humanities and Economics, Poland

    Leszek Rutkowski

  2. German Research Center of Artificial Intelligence (DFKI), Germany

    Jörg H. Siekmann

  3. Institute of Automatics, AGH University of Science and Technology, Al. Mickiewicza 30, PL-30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  4. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Initiative in Soft Computing (BISC), 94720-1776, Berkeley, CA

    Lotfi A. Zadeh

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© 2004 Springer-Verlag Berlin Heidelberg

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da Silva, I.N., Ulson, J.A.C., de Souza, A.N. (2004). Implementation of Two-Stage Hopfield Model and Its Application in Nonlinear Systems. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds) Artificial Intelligence and Soft Computing - ICAISC 2004. ICAISC 2004. Lecture Notes in Computer Science(), vol 3070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24844-6_148

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  • DOI: https://doi.org/10.1007/978-3-540-24844-6_148

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22123-4

  • Online ISBN: 978-3-540-24844-6

  • eBook Packages: Springer Book Archive

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