Generalized Associative Memory Models: Their Memory Capacities and Potential Application
Teddy N. Yap, Jr.* and Arnulfo P. Azcarraga**
*Software Technology Department College of Computer Studies De La Salle University Professional Schools, Inc., 2401 Taft Avenue 1004 Manila, Philippines
**Program for Research into Intelligent Systems School of Computing
Department of Computer Science, 3 Science Drive 2, 117543 Republic of Singapore
De La Salle University, CanlubangLaguna, Philippines
The Hopfield and bi-directional associative memory (BAM) models are well developed and carefully studied models for associative memory that are patterned after the memory structure of the animal brain. Their basic limitation is that they can only perform associations between at most two sets of patterns. Several different models for generalized associative memory are proposed. These models are all extensions or generalizations of the Hopfield and BAM models that can perform multiple associations. Extensive software simulations are conducted to evaluate the different models, using the memory capacity as basis for comparing their performance. Lastly, potential application of these models as data fusion systems is explored.