An associative memory system for incremental learning and temporal sequence

Furao Shen, Hui Yu, Wataru Kasai, Osamu Hasegawa

Abstract: An associative memory (AM) system is proposed to realize incremental learning and temporal sequence learning. The proposed system is constructed with three layer networks: The input layer inputs key vectors, response vectors, and the associative relation between vectors. The memory layer stores input vectors incrementally to corresponding classes. The associative layer builds associative relations between classes. The proposed method can incrementally learn key vectors and response vectors; store and recall both static information and temporal sequence information; and recall information from incomplete or noise-polluted inputs. Experiments using binary data, real-value data, and temporal sequences show that the proposed method works well.