Title :
Two coding strategies for bidirectional associative memory
Author :
Wang, Yeou-Fang ; Cruz, Jose B., Jr. ; Mulligan, James H., Jr.
Author_Institution :
Dept. of Electr. & Comput. Eng., California Univ., Irvine, CA, USA
fDate :
3/1/1990 12:00:00 AM
Abstract :
Enhancements of the encoding strategy of a discrete bidirectional associative memory (BAM) reported by B. Kosko (1987) are presented. There are two major concepts in this work: multiple training, which can be guaranteed to achieve recall of a single trained pair under suitable initial conditions of data, and dummy augmentation, which can be guaranteed to achieve recall of all trained pairs if attaching dummy data to the training pairs is allowable. In representative computer simulations, multiple training has been shown to lead to an improvement over the original Kosko strategy for recall of multiple pairs as well. A sufficient condition for a correlation matrix to make the energies of the training pairs be local minima is discussed. The use of multiple training and dummy augmentation concepts are illustrated, and theorems underlying the results are presented
Keywords :
content-addressable storage; encoding; neural nets; Kosko strategy; bidirectional associative memory; correlation matrix; dummy augmentation; encoding strategy; multiple training; neural nets; sufficient condition; Associative memory; Computer simulation; Encoding; Iterative decoding; Joining processes; Magnesium compounds; Manufacturing; Neural networks; Subcontracting; Sufficient conditions;
Journal_Title :
Neural Networks, IEEE Transactions on