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Convergence of the Projection-Based Generalized Neural Network and the Application to Nonsmooth Optimization Problems

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Book cover Advances in Neural Networks - ISNN 2010 (ISNN 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6063))

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Abstract

This paper introduces a projection-based generalized neural network, which can be used to solve a class of nonsmooth convex optimization problems. It generalizes the existing projection neural networks for solving the optimization problems. In addition, the existence and convergence of the solution for the generalized neural networks are proved. Moreover, we discuss the application to nonsmooth convex optimization problems. And two illustrative examples are given to show the efficiency of the theoretical results.

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Author information

Authors and Affiliations

  1. School of Science, Jiangnan University, Wuxi, 214122, P.R. China

    Jiao Liu, Yongqing Yang & Xianyun Xu

Authors
  1. Jiao Liu
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  2. Yongqing Yang
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  3. Xianyun Xu
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, 200240, Shanghai, China

    Liqing Zhang

  2. Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800, Dongchuan Road, 200240, Shanghai, China

    Bao-Liang Lu

  3. Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water bay, Kowloon, Hong Kong, China

    James Kwok

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© 2010 Springer-Verlag Berlin Heidelberg

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Liu, J., Yang, Y., Xu, X. (2010). Convergence of the Projection-Based Generalized Neural Network and the Application to Nonsmooth Optimization Problems. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6063. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13278-0_33

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  • DOI: https://doi.org/10.1007/978-3-642-13278-0_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13277-3

  • Online ISBN: 978-3-642-13278-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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