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A New Neural Network Algorithm for Clique Vertex-Partition Problem

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Advances in Neural Networks – ISNN 2004 (ISNN 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3173))

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Abstract

Abstract. In this paper, by adding a nonlinear self-feedback to the maximum neural network (MNN), we propose a new algorithm for the clique vertexpartition problem that introduces richer and more flexible dynamics and can prevent the network from getting stuck at local minima. A large number of instances have been simulated to verify the proposed algorithm.

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References

  1. Funabiki, N., Takefuji, Y., Lee, K.C.: A Neural Network Parallel Algorithm for Clique Vertex-Partition Problems. International Journal of Electronics 72(3), 357–372 (1992)

    Article  Google Scholar 

  2. Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP Completeness. Freeman, W.H., New York

    Google Scholar 

  3. Tseng, C.J., Siewiorek, D.P.: Facet: A Procedure for the Automated Synthesis of Digital Systems. In: Proceeding of the 20th ACM/IEEE Design Automated Conference, pp. 490–496 (1983)

    Google Scholar 

  4. Takefuji, Y., Wang, J.: Neural Computing for Optimization and Combinatorics. World Scientific, Singapore (1996)

    MATH  Google Scholar 

  5. Takefuji, Y., Lee, K., Aiso, H.: H.: An Artificial Maximum Neural Network: A Winner-Take-All Neuron Model Forcing the State of the System in a Solution Domain. Biological Cybernetics 67, 243–251 (1992)

    Article  MATH  Google Scholar 

  6. Takenaka, Y., Funabiki, N., Higashino, T.: A Proposal Neural Filter: A Constraint Resolution Scheme of Neural Networks for Combinatorial Optimization Problems. IEICE Trans. Fundamentals E83-A(9), 1815–1823 (2000)

    Google Scholar 

  7. Ikeguchi, T., Aihara, K., Itoh, K.: A Novel Chaotic Search for Quadratic Assignment Problems. European Journal of Operational Research 139, 543–556 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  8. Chen, L., Aihara, K.: Chaotic Simulated Annealing by a Neural Network Model With Transient Chaos. Neural Networks 8(6), 915–930 (1995)

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty of Engineering, Toyama University, Toyama-shi, 930-8555, Japan

    Jiahai Wang, Xinshun Xu, Zheng Tang, Weixing Bi, Xiaoming Chen & Yong Li

Authors
  1. Jiahai Wang
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  2. Xinshun Xu
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  3. Zheng Tang
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  4. Weixing Bi
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  5. Xiaoming Chen
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  6. Yong Li
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Editor information

Editors and Affiliations

  1. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, China

    Fu-Liang Yin

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

    Jun Wang

  3. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, Liaoning, China

    Chengan Guo

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© 2004 Springer-Verlag Berlin Heidelberg

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Wang, J., Xu, X., Tang, Z., Bi, W., Chen, X., Li, Y. (2004). A New Neural Network Algorithm for Clique Vertex-Partition Problem. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_71

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  • DOI: https://doi.org/10.1007/978-3-540-28647-9_71

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22841-7

  • Online ISBN: 978-3-540-28647-9

  • eBook Packages: Springer Book Archive

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