Abstract
The strategies for an associative recall can be based on associative memory models. However, the performance of standard associative memories is very sensitive to the number of stored patterns and their mutual correlations. With respect to huge amounts of spatial patterns (mostly correlated) to be processed, we have focused on an arbitrary number of associative memories grouped into several layers (Hierarchical Associative Memories – HAM). In the newly presented HAM2-model, the patterns are hierarchically grouped according to the “previous-layer” patterns. The HAM2-model uses the information recalled by the “previous-layer” to find an appropriate subset of “next-level” associative memories. To evaluate the performance of the HAM2-model, extensive simulations are carried out. The experimental results show the recall ability of the model in the area of associative pattern recall.KeywordsAssociative MemoryTraining PatternAcceptable ErrorDifference PatternPattern RateThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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