DocumentCode :
2175095
Title :
Dense memory with high order neural networks
Author :
Jeffries, Clark
Author_Institution :
Dept. of Math. Sci., Clemson Univ., SC, USA
fYear :
1989
fDate :
26-28 Mar 1989
Firstpage :
436
Lastpage :
439
Abstract :
The author presents a specific high-order neural network design that can store using n neutrons, any number M, 1⩽ M⩽2n, of any of the binomial n-strings; in a schematic representation the model requires only 5n+M (1+2n) edges. With sufficiently high gains, the only stable attractors are the memories. Thus the memory model amounts to a solution of a version of the fundamental memory problem. The memory model can be used in error-correcting decoding of any binary string code and, in particular, has been used to correct single errors in a linear code with n=7 and single and double errors in a nonlinear code with n=11
Keywords :
decoding; error correction; memory architecture; neural nets; binary string code; dense memory; error-correcting decoding; memory model; neural networks; nonlinear code; Associative memory; Convergence; Decoding; Error correction codes; Linear code; Neural networks; Neurons; State-space methods; Tellurium;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
System Theory, 1989. Proceedings., Twenty-First Southeastern Symposium on
Conference_Location :
Tallahassee, FL
ISSN :
0094-2898
Print_ISBN :
0-8186-1933-3
Type :
conf
DOI :
10.1109/SSST.1989.72506
Filename :
72506
Link To Document :
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