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Hybrid Hopfield Architecture for Solving Nonlinear Programming Problems

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Neural Information Processing (ICONIP 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5863))

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Abstract

This paper presents a neurogenetic approach for solving nonlinear programming problems. Genetic algorithm must its popularity to make possible cover nonlinear and extensive search spaces. Neural networks with feedback connections provide a computing model capable of solving a large class of optimization problems. The association of a modified Hopfield network with genetic algorithm guarantees the convergence of the system to the equilibrium points, which represent feasible solutions for nonlinear programming problems.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, State University of Feira de Santana, CEP 44031-460, Feira de Santana, BA, Brazil

    Fabiana Cristina Bertoni & Ivan Nunes da Silva

  2. Department of Electrical Engineering, University of São Paulo, CP 359, CEP 13566.590, São Carlos, SP, Brazil

    Fabiana Cristina Bertoni & Ivan Nunes da Silva

Authors
  1. Fabiana Cristina Bertoni
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  2. Ivan Nunes da Silva
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Editor information

Editors and Affiliations

  1. Department of Electronic Engineering, City University of Hong Kong, Hong Kong,  

    Chi Sing Leung

  2. School of Electrical Engineering and Computer Science, Kyungpook National University, 1370 Sankyuk-Dong, Puk-Gu, 702-701, Taegu, Korea

    Minho Lee

  3. School of Information Technology, King Mongkut’s University of Technology Thonburi, 126 Pracha-U-Thit Rd., Bangmod, Thungkru, 10140, Bangkok, Thailand

    Jonathan H. Chan

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© 2009 Springer-Verlag Berlin Heidelberg

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Bertoni, F.C., da Silva, I.N. (2009). Hybrid Hopfield Architecture for Solving Nonlinear Programming Problems. In: Leung, C.S., Lee, M., Chan, J.H. (eds) Neural Information Processing. ICONIP 2009. Lecture Notes in Computer Science, vol 5863. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10677-4_30

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  • DOI: https://doi.org/10.1007/978-3-642-10677-4_30

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10676-7

  • Online ISBN: 978-3-642-10677-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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