A LEARNING AND FORGETTING ALGORITHM IN ASSOCIATIVE MEMORIES - THE EIGENSTRUCTURE METHOD

In this paper we develop a design technique for associative memories with learning and forgetting capabilities via artificial feedback neural networks. The proposed synthesis technique utilizes the eigenstructure method. Networks generated by the present method are capable of learning new patterns as well as forgetting existing patterns without the necessity of recomputing the entire interconnection weights and external inputs. In many respects, the present results constitute significant improvements over the outer product method, the projection learning rule, and the pseudo-inverse method with stability constraints. Several specific examples are given to illustrate the strengths and weaknesses of the methodology advocated.