The asymptotic memory capacity of the generalized Hopfield network

Neural Networks

Volumn 12, Issue 9, 1999, Pages 1207-1212

  Ma, Jinwen ^a  

^a SHANTOU UNIVERSITY   (China)

Author keywords

Associative memory; Asymptotic memory capacity; Hopfield network; Neural network; Perceptron learning algorithm

Indexed keywords

ALGORITHMS; ASSOCIATIVE STORAGE; LEARNING SYSTEMS; STORAGE ALLOCATION (COMPUTER);

ASYMPTOTIC MEMORY CAPACITY; GENERALIZED HOPFIELD NETWORK;

NEURAL NETWORKS;

ALGORITHM; ANALYTIC METHOD; ARTICLE; ASSOCIATIVE MEMORY; LEARNING; NERVE CELL NETWORK; NONHUMAN; PERCEPTION; PRIORITY JOURNAL; PROBABILITY; SAMPLING; THEORY;

EID: 0032852881     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0893-6080(99)00042-8     Document Type: Article

References (12)

1. Abu-Mostafa Y.S., Jacques J.S. Information capacity of Hopfield model. IEEE Transactions on Information Theory. 31:1985;451-464.
2. Cover T.M. Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition. IEEE Transactions on Electronic Computer. 13:1965;326-334.
3. Gardner E. The space of interactions in neural network models. Journal of Physics A: Mathematical and General. 21:1988;257-270.
4. Hopfield J.J. Neural networks and physical system with emergent collective computational abilities. Proceedings of the National Academy of Science, USA. 79:1982;2554-2558.
5. Komlos J. On determinant of (0,1) matrices. Studia Science Mathematics Hungarica. 2:1967;7-21.
6. Ma J. The stability of the generalized Hopfield networks in randomly asynchronous mode. Neural Networks. 10:1997;1109-1116.
7. McMliece R.E., et al. The capacity of the Hopfield associative memory. IEEE Transactions on Information Theory. 33:1987;461-483.
8. Pollard D. Convergence of stochastic processes. 1989;Springer, Berlin.
9. Rosenblatt F. Principles of neurodynamics. 1962;Spartan Books, New York.
10. Venkatesh, S.S., 1987. Linear maps with point rules: applications to pattern classification and associative memory. PhD dissertation, California Institute of Technology, Pasadena.
11. Venkatesh S.S., Psaltis D. Linear and logarithmic capacities in associative memory. IEEE Transaction on Information Theory. 35:1989;558-568.
12. Wang T., et al. Learning algorithm in unequally-weighted associative memory. Pattern Recognition and Artificial Intelligence (Chinese). 6:(1):1993;43-48.