Code Recognition with Neural Network Dynamical Systems
Abstract
A class of dynamical systems called neural networks can be used to solve a variety of finite choice problems including the problem of correcting binary code.
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References
G.
S. Grossberg, Nonlinear neural networks; principles, mechanisms, and architectures, Neural Networks, 1 (1988), 17–61
GM.
C. L. Giles, T. P. Maxwell, Learning, invariance, and generalization in high order neural networks, Appl. Optics, 26 (1987), 4972–4978
H.
J. J. Hopfield, Neurons with graded response have collective computational properties like those of two-state neurons, Proc. Nat. Acad. Sci. U.S.A., 81 (1984), 3088–3092
J.
C. Jeffries, Mathematical analysis of neural networks used in the solution of set selection problems, Proc. Third IEEE International Symposium on Intelligent Control, 1988
JP.
C. Jeffries, P. Protzel, High-order neural models for error correction of binary codes, 1990, Proc. SPIE Symposium Intelligent Systems, Orlando, FL, April, to appear
JVDD.
Clark Jeffries, P. van den Driessche, Hypergraph analysis of neural networks, Phys. D, 39 (1989), 315–321
LD.
Y. Lee, G. Doolen, H. Chen, G. Sun, T. Maxwell, H. Lee, C. L. Giles, Machine learning using a higher order correlation network, Phys. D, 22 (1986), 276–306
LMP.
J. H. Li, A. Michel, W. Porod, Qualitative analysis and synthesis of a class of neural networks, IEEE Trans. Circuits and Systems, 35 (1988), 976–986
MS.
F. J. MacWilliams, N. J. A. Sloane, The Theory of Error-Correcting Codes, North-Holland, Amsterdam, New York, 1977
M.
C. Mead, Analog VLSI and Neural Systems, Addison–Wesley, Reading, MA, 1989
PT.
E. Page, G. Tagliarini, Algorithm development for neural networks, Proc. SPIE Symposium Innovative Sci. Tech., 880 (1988), 11–18
P.
Vera Pless, Introduction to the theory of error-correcting codes, John Wiley & Sons Inc., New York, 1982xi+169
PF.
R. Plonsey, D. Fleming, Bioelectric Phenomena, McGraw–Hill, New York, 1969
PPH.
D. Psaltis, C. H. Park, J. Hong, higher order associative memories and their optical implementations, Neural Networks, 1 (1988), 149–163
T.
G. Tagliarini, Undesirable equilibria in systematically designed neural networks, Proc. IEEE Southeastcon, 1989, 63–67
TP.
G. Tagliarini, E. Page, Solving constraint satisfaction problems with neutral networks, Proc. IEEE International. Conference on Neural Networks, 3 (1987), 741–747
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Copyright © 1990 Society for Industrial and Applied Mathematics.
History
Submitted: 26 April 1989
Accepted: 8 December 1989
Published online: 18 July 2006
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Cited By
- A universal structure for artificial neural networksIEEE International Conference on Neural Networks
- Alleviating the opacity of neural networksProceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)
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