Synthesis of adaptive memories with neural networks
The general formulation of bidirectional associative memories presents certain difficulties when the associations of pairs of patterns do not suppose a local energy minimun. To avoid these problems, the present paper describes an adaptive scheme which al lows the correlation matrix to be modified so as to reach the energy minimun while at the same time identifying the input patterns. The strategy described here allows the adaptation of the matrix to be performed for each external input, so that it can henceforth be described as a supervised type of training scheme. A consequence is its synthesis by means of neural networks with both the BAM and the adaptive mechanism itself integrated in distinct layers, allowing either of them to be changed without altering the other.
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