Sequential learning for associative memory using Kohonen feature map

Abstract

We propose a sequential learning algorithm for an associative memory based on Kohonen feature map. In order to store new information without retraining weights on previously learned information, weights fixed neurons and weights semi-fixed neurons are used in the proposed algorithm. Owing to the semi-fixed neurons, the associative memory becomes structurally robust. Moreover, it has the following features: 1) it is robust for noisy inputs; 2) it has high storage capacity; and 3) it casts deal with one-to-many associations.