A new input-output function for binary hopfield neural networks

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1606, Issue , 1999, Pages 311-320

  Galan G ^a   Munoz J ^a  

^a UNIVERSITY OF MÁLAGA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

BINS; GLOBAL OPTIMIZATION; OPTIMIZATION; PROBLEM SOLVING;

CONVERGENCE CONDITIONS; CONVERGENCE THEOREM; INPUT-OUTPUT FUNCTION; ITS APPLICATIONS; MCCULLOCH-PITTS; N-QUEENS PROBLEMS; OPTIMIZATION PROBLEMS; PERFORMANCE COMPARISON;

HOPFIELD NEURAL NETWORKS;

EID: 0001018045     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/BFb0098187     Document Type: Conference Paper

References (16)

1. J. J. Ilopfield and D. W. Tank, “Neural computation of decisions in optimization problems”, Biol. Cybern., vol. 52, 1985, pp. 141-152.
2. J. J. Ilopfield, “Neural Networks and physical systems with emergent collective computational abilities” in Proc. Ac. Academy Sci. USA, vol. 79, 1982, pp. 2554-2558.
3. Y. Takefuji, K.C. Lee, “An artificial hysteresis binary neuron: a model impressing the oscillatory behaviors of neural dynamics”, Biol. Cybem., vol. 64, 1991, pp. 353-356.
4. Y. Takefuji, K.G. Lee, “Artificial neural networks for four-colouring map problems and K-colorability problems”, IEEE Dans. Circuits Syst… vol. 38, 1991, pp. 320-333.
5. Y. Takefuji," Neural Network Parallel computing”, Kluwcr Academic Publishers, 1992.
6. N. Funabiki, Y. Takenaka, S. Nishikawa, “A maximun neural network approach for N-quecns problem”, Biol. Cybem., vol. 70, 1997, pp. 251-255.
7. M. Ohta, A. Ogihara, K. Fukunaga, “Binary neural network with self-feedback and its application to N-queens problem”, IEICE Dunn. inf. & syst., vol. E77-D, no,4, 1994, pp. 459-465.
8. Mandziuk, J., “Solving the N-quccns problem with a binary Hoplicld-type network”, Mol. Cybern, vol. 72, 1995, pp. 439-445.
9. Sim, Y., “A generalized updating rule for modified Iloplield neural network for quadratic optimization”, Neurocomputing, vol. 19, 1998, pp. 133-143.
10. M. Peng, N.K. Gupta, A.F. Armitage, “An investigation into the improvement, of local minima of the Iloplield network”, Neural Networks, vol. 9, 1990, pp. 1241-1253.
11. M. Tateishi, S. Tanmra, “Comments on’Artificial neural networks for four-colouring map problems and K-coloi ability problems’”, IEEE Trans. Circuits Syst. I: Fundamental Theory Appli-rat., vol. 41, 1994, pp. 248-249.
12. L. Wang, “Discrete-time convergence theory and updating rules for neural networks with energy functions”, IEEE Dans. Neural Networks, vol 8, 1997, pp. 445-447.
13. O. K. Cheng, T. Lee, “On the convergence of Neural Network for higher order programming”, Proc. of International Joint. Conference on Neural Networks, 1993, pp. 1507-1511.
14. J. K. Paik, A.K. Katsaggelos, “Image restoration using a modified Hopfield network”, IEEE Trans. Image Process., vol 30(7), 1992, pp. 49-63.
15. Y. Sun, J.G. Li, S.Y. Yu, “Improvement on performance of modified Ilopfield network for image restoration", IEEE Dans. Image. Process., vol 5, 1995, pp. 688-692.
16. Y. T. Zhou, R. Chellappa, A. Vaid, B.K. Jenkins, “Image restoration using a neural network”, IEEE Dans. Acoust. Speech Signal Process., vol 30(7), 1988, pp. 1141-1151.