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Neural Networks for Nonconvex Nonlinear Programming Problems: A Switching Control Approach

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Advances in Neural Networks – ISNN 2005 (ISNN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3496))

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Abstract

In this paper, neural networks based on switching control approach are proposed, which is aimed at solving in real time a much wider class of nonconvex nonlinear programming problems where the objective function is assumed to satisfy only the weak condition of being regular functions. By using the gradient of the involved functions, the switching control approach proposed is shown to obey a gradient system of differential equation, and its dynamical behavior, trajectory convergence in finite time, and optimization capabilities, for nonconvex problems, are rigorously analyzed in the framework of the theory of differential equations, which are expected to enable to gain further insight on the geometrical structure of the energy landscape (objective function) of each specific class of nonlinear programming problems which is dealt with.

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Author information

Authors and Affiliations

  1. College of Electrical Engineering, Hohai University, Nanjing, 210098, China

    Changyin Sun

  2. Research Institute of Automation, Southeast University, Nanjing, 210096, China

    Changyin Sun & Chunbo Feng

Authors
  1. Changyin Sun
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  2. Chunbo Feng
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. The Key Laboratory of Optoelectric Technology & Systems, Ministry of Education, China

    Xiaofeng Liao

  3. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

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© 2005 Springer-Verlag Berlin Heidelberg

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Sun, C., Feng, C. (2005). Neural Networks for Nonconvex Nonlinear Programming Problems: A Switching Control Approach. In: Wang, J., Liao, X., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427391_111

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  • DOI: https://doi.org/10.1007/11427391_111

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25912-1

  • Online ISBN: 978-3-540-32065-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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