Improved projection Hopfield network for the quadratic assignment problem

International Journal of Information Technology and Decision Making

Volumn 7, Issue 1, 2008, Pages 53-70

  Tatsumi, Keiji ^a   Tanino, Tetsuzo ^a  

^a OSAKA UNIVERSITY   (Japan)

Author keywords

Continuous valued Hopfield neural networks; Convergence property; Projection method; Quadratic assignment problem; Stability

Indexed keywords

EID: 43949143927     PISSN: 02196220     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0219622008002818     Document Type: Article

References (16)

1. J. J. Hopfield and D. Tank, Neural computation of decisions in optimization problems, Biological Cybernetics 52 (1985) 141-152.
2. X.-S. Zhang, Neural Networks in Optimization (Kluwer Academic Publishers, London, 2000).
3. S. Matsuda, Optimal Hopfield network for combinatorial optimization with linear cost function, IEEE Transactions on Neural Networks 9 (1998) 1319-1330.
4. S. Matsuda, An optimal Hopfield network for combinatorial optimization and its approximate realization, IEICE Trans. E83-A (2000) 1211-1221.
5. A. H. Gee, Problem solving with optimization networks, Ph.D. dissertation, Queen's College, Cambridge, UK (1993).
6. K. Smith, M. Palaniswami and M. Krishnamoorthy, Neural techniques for combinatorial optimization with applications, IEEE Transactions on Neural Networks 9 (1998) 1301-1317.
7. T. C. Koopmans and M. J. Beckmann, Assignment problems and the location of economic activities, Econometrica 25 (1957) 53-76.
8. W. Huang, K. K. Lai, Y. Nakamori and S. Wang, Forecasting foreign exchange rates with artificial neural networks: A review, International Journal of Information Technology & Decision Making 3 (2004) 145-166.
9. L. Yu, K. K. Lai and S.-Y. Wang, Currency crisis forecasting with general regression neural networks, International Journal of Information Technology & Decision Making 5 (2006) 437-454.
10. B. W. Wah and M.-L. Qian, Constrained formulations and algorithms for predicting stock prices by recurrent FIR, neural networks, International Journal of Information Technology & Decision Making 5 (2006) 639-658.
11. C. A. Floudas and V. Visweswaran, Quadratic optimization, in Handbook of Global Optimization, eds. R. Horst and P. M. Pardalos (Kluwer Academic Publishers, 1995), pp. 217-269.
12. Y. Uesaka, On the stability of neural networks with the energy function of binary variables, Tech. Rep. of Inst. of Electronics, Information and Communication Engineers, PRU 88-6 (1998).
13. D. Bertsekas, Nonlinear Programming, 2nd edn. (Athena, Scientific, 1995).
14. Y. Shimazu, M. Fukushima and T. Ibaraki, A successive over-relaxation method for quadratic programming problems with interval constraints, Journal of the Operations Research Society of Japan 36 (1993) 73-89.
15. A. L. Yuille and J. J. Kosowsky, Statistical physics algorithms that converge, Neural Computation 6 (1994) 341-356.
16. R. E. Burkard, S. E. Karisch and F. Rendl, QAPLIB-A quadratic assignment problem library, Journal of Global Optimization 10 (1997) 391-403.