Bidirectional associative memories
IEEE Transactions on Systems, Man and Cybernetics
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In this paper, we propose a sequential learning algorithm for an associative memory based on Self-Organizing Map (SOM). In order to store newinformation without retraining weights on previously learned information, weights fixed neurons and weights semi-fixed neurons are used in the proposed algorithm. In addition, when a new input is applied to the associative memory, a part of map is reconstructed by using a small buffer. Owing to this remapping, a topology preserving map is constructed and the associative memory becomes structurally robust. Moreover, it has much better noise reduction effect than the conventional associative memory.