A shrinking chaotic maximum neural network for maximum clique problem

International Journal of Innovative Computing, Information and Control

Volumn 5, Issue 5, 2009, Pages 1213-1229

  Yi, Junyan ^a   Yang, Gang ^a   Gao, Shangce ^a   Tang, Zheng ^a  

^a UNIVERSITY OF TOYAMA   (Japan)

Author keywords

Maximum clique problem; Maximum neural network; Shrinking mechanism

Indexed keywords

EID: 63849249764     PISSN: 13494198     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (16)

1. R. M. Karp, Reducibility among combinatorial problems, in Complexity of Computer Computations, pp.85-104, Plenum, New York, 1972.
2. N. Funabiki and S. Nishikawa, Comparisons of energy-descent optimization algorithms for maximum clique problem, IEICE Trans. Fundamentals, vol.E79-A, no.4, pp.452-460, April 1996.
3. Y. Abe, M. Konishi and J. Imai, Neural network based diagnosis system for looper height controller of hot strip mills, Int. J. Innovative Computing, Information and Control, vol.3, no.4, pp.919-935, 2007.
4. A. Fekih, H. Xu and F. Chowdhury, Neural networks based system identification techniques for model based fault detection of nonlinear systems, Int. J. Innovative Computing, Information and Control, vol.3, no.5, pp.1073-1085, 2007.
5. F. Takeda, Y. Shiraishi and T. Sanechika, Alarm sound classification system of oxygen concentrator by using neural network, Int. J. Innovative Computing, Information and Control, vol.3, no.1, pp.211-222, 2007.
6. J. J. Hopfield and D. W. Tank, Neural computation of decisions in optimization problems, Biological Cybernetics, vol.52, pp.141-152, 1985.
7. A. Jagota, Approximating maximum clique with Hopfield network, IEEE Trans. Neural Network, vol.6, no.3, pp.724-735, 1995
8. D. S. Johnson, M. A. Trick, and D. J. Johnson, Cliques, coloring, and satisfiability: Second dimacs implementation challenge, Dimacs Series in Discrete Mathematics and Theoretical Computer Science, vol.26, pp.11-13, 1993.
9. Y. Takefuji, K. Lee and H. Aiso, An artificial maximum neural network: A winner-take-all neuron model forcing the state of the system in a solution domain, Biol. Cybernet. vol.67, no.3, pp.243-251, 1992.
10. K. C. Lee, N.Funabiki and Y. Takefuji, A parallel improvement algorithm for the bipratite subgraph problem, IEEE Trans. Neural Networks, vol.3, no.1, pp139-145, 1992.
11. Y. Takenaka, N. Funabiki and T. Higashino, A proposal neural filter: A constraint resolution scheme of neural networks for combinatorial optimization problems, IEICE Trans. Fundamentals, vol.E83-A, no.9, pp.1815-1823, 2000.
12. L. Chen and K. Aihara, Chaotic simulated annealing by a neural network model with transient chaos, Neural Networks, vol.8 no.6 pp.915-930, 1995.
13. J. Wang, Z. Tang and R. Wang, A chaotic maximum neural network for maximum clique problem, IEICE Trans. INF. & SYST., vol.E87-D, no.7, pp.1953-1961, 2004.
14. K. C. Lee, N. Funabiki, Y. B. Cho and Y. Takefuji, A parallel neural network computing for the maximum clique problem, Proc. of the International Joint Conference on Neural Networks, Singapore, pp.905-910, 1991.
15. Y. Takefuji and J. Wang, Neural Computing for Optimization and Combinatorics, Chapter 3, World Scientific, Singapore, 1996.
16. T. Kwok and K. A. Smith, Experimental analysis of chaotic neural network models for combinatorial optimization under a unifying framework, Neural Networks, vol.13, pp.731-744, 2000.