Sequential Learning for SOM Associative Memory with Map Reconstruction

Abstract

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.