Tariq Samad, Paul Harper

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


An n-bit associative memory is constructed using a network of n sigmoid units. Each unit is connected to every other unit with unidirectional weight, and to itself. A threshold is also associated with each unit. The presence of self-weights allows the storage of memories that do not conform to the linear predictability constraint, such as parity-encoded memories. The storage of memories is effected by assigning appropriate values to the weights and thresholds. A variant of the generalized delta rule, which has been used previously in back-propagation, is used for determining these values. The present network is distinct from the back-propagation model in two ways: it consists of one rather than several layers, and it has feedback connections. A consequence of the feedback connections is that a relaxation phase is required in which the unit values are iteratively recomputed until they stabilize. A relaxation procedure analogous to simulated annealing but applicable to networks of real-valued units is described that greatly improves the performance of the memory. Various performance figures are presented.

Original languageEnglish (US)
Title of host publicationUnknown Host Publication Title
EditorsMaureen Caudill, Charles T. Butler, San Diego Adaptics
PublisherSOS Printing
StatePublished - Dec 1 1987

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