A LEARNING AND FORGETTING ALGORITHM IN ASSOCIATIVE MEMORIES - RESULTS INVOLVING PSEUDO-INVERSES

This paper develops a design technique for associative memories with learning and forgetting abilities via artificial feedback neural networks. The proposed method utilizes the theory of large-scale interconnected dynamical systems, instead of the usual energy methods. Networks synthesized by the design method advanced herein are capable of learning new patterns as well as forgetting old patterns without recomputing the entire interconnection matrix. The present method, which employs the properties of pseudo-inverse matrices to iteratively solve systems of linear equations, provides significant improvements over the outer product method and the projection learning rule. Several specific examples are given to illustrate the strengths and weaknesses of the methodology advocated.