Associative memory design using overlapping decomposition and generalized brain-state-in-a-box neural networks.
This paper is concerned with large scale associative memory design. A serious problem with neural associative memories is the quadratic growth of the number of interconnections with the problem size. An overlapping decomposition algorithm is proposed to attack this problem. Specifically, a pattern to be processed is decomposed into overlapping sub-patterns. Then, neural sub-networks are constructed that process the sub-patterns. An error correction algorithm operates on the outputs of each sub-network in order to correct the mismatches between sub-patterns that are obtained from the independent recall processes of individual sub-networks. The performance of the proposed large scale associative memory is illustrated using two-dimensional images. It is shown that the proposed method reduces the computing cost of the design of the associative memories compared with non-interconnected associative memories.
Oh
Cheolhwan
C
School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA. oh2@ecn.purdue.edu
Zak
Stanislaw H
SH
D000465:Algorithms; D001185:Artificial Intelligence; D001244:Association; D001921:Brain / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D008568:Memory / Q000502:physiology*; D008959:Models, Neurological; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D011336:Probability; D013997:Time Factors