Employing a Multiple Associative Memory Model for Temporal Sequence Reproduction

Aluizio F. R. AraújoMarcelo Vieira

This paper introduces an associative memory model which associates n-tuples of patterns, employs continuous and limited pattern representation, performs both auto- and heteroassociative tasks, and has adaptable correlation matrices. This model called Temporal Multidirectional Associative Memory (TMAM) is an adaptation of the Multidirectional Associative Memory (MAM) which includes autoassociative links, real activation functions, and supervised learning rules. The experimental results suggest that the model presents fast learning, improves storage capacity of MAM, reproduces trained temporal sequences, interpolates states within a trained sequence, extrapolates states in both extremities of a given sequence, and accommodates sequences of different number of steps.

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