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.
CITATION STYLE
Hattori, M., Arisumi, H., & Ito, H. (2001). Sequential learning for SOM associative memory with map reconstruction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2130, pp. 477–484). Springer Verlag. https://doi.org/10.1007/3-540-44668-0_67
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