Improvements of complex-valued Hopfield associative memory by using generalized projection rules

IEEE Transactions on Neural Networks

Volumn 17, Issue 5, 2006, Pages 1341-1347

  Lee, Donq Liang ^a  

^a MING CHUAN UNIVERSITY   (Taiwan)

Author keywords

Complex valued Hopfield associative memory (CVHAM); Generalized projection rule (GPR); Spurious memory

Indexed keywords

ASSOCIATIVE STORAGE; KNOWLEDGE BASED SYSTEMS; MATHEMATICAL MODELS;

COMPLEX-VALUED HOPFIELD ASSOCIATIVE MEMORY (CVHAM); DESIGN METHODS; SPURIOUS MEMORY;

HOPFIELD NEURAL NETWORKS;

ALGORITHM; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; CLUSTER ANALYSIS; COMPUTER ANALYSIS; LETTER; METHODOLOGY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CLUSTER ANALYSIS; COMPUTING METHODOLOGIES; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED;

EID: 33750098301     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2006.878786     Document Type: Article

References (13)

1. J. J. Hopfield, "Neurons with graded response have collective computational properties like those of two-state neurons," in Proc. Nat. Acad. Sci., 1984, vol. 81, pp. 3088-3092.
2. L. Personnaz, I. Guyon, and G. Dreyfus, "Collective computational properties of neural networks: New learning mechanisms," Phys. Rev., vol. A34, pp. 4217-4228, 1986.
3. L. Personnaz, I. Guyon, G. Dreyfus, and G. Toulouse, "A biologically constrained learning mechanism in networks of formal neurons," J. Stat. Phys., vol. 43, pp. 411-422, May 1986.
4. J. M. Zurada, I. Cloete, and E. van der Poel, "Generalized Hopfield networks with multiple stable states," Neurocomput., vol. 13, pp. 135-149, 1996.
5. S. Jankowski, A. Lozowski, and J. M. Zurada, "Complex-valued multistate neural associative memory," IEEE Trans. Neural Netw., vol. 7, no. 6, pp. 1491-1496, Nov. 1996.
6. M. K. Müezzinoǧlu, C. Güzeliş, and J. M. Zurada, "A new design method for the complex-valued multistate Hopfield associative memory," IEEE Trans. Neural Netw., vol. 14, no. 4, pp. 891-899, Jul. 2003.
7. D. L. Lee, "Improving the capacity of complex-valued neural networks with a modified gradient descent learning rule," IEEE Trans. Neural Netw., vol. 12, no. 2, pp. 439-443, Mar. 2001.
8. _, "Relaxation of the stability condition of the complex-valued neural networks," IEEE Trans. Neural Netw., vol. 12, no. 5, pp. 1260-1262, Sep. 2001.
9. A. Albert, Regression and the Moore-Penrose Pseudoinverse. New York: Academic, 1972.
10. P. Lancaster and M. Tismenetsky, The Theory of Matrices: With Applications, 2nd ed. New York: Academic, 1985.
11. M. Taketa and J. W. Goodman, "Neural networks for computation: Number representations and programming complexity," Appl. Opt., vol. 25, pp. 3033-3046, 1986.
12. G. Costantini, D. Casali, and R. Pefetti, "Neural associative memory storing gray-coded gray-scale images," IEEE Trans. Neural Netw, vol. 14, no. 3, pp. 703-707, May 2003.
13. J. Si and A. N. Michel, "Analysis and syntehsis of discrete-time neural networks with multilevel threshold functions," in Proc. Int. Symp. Circuits Systems (ISCAS), 1991, pp. 1461-1464.