Unlearning algorithm in associative memories: eigenstructure method

Unlearning capabilities are incorporated into a synthesis procedure for a class of discrete-time neural networks. The proposed technique increases storing capacity while maximizing the domain of attraction of each desired pattern to be stored. Making use of learning, forgetting, and unlearning capabilities, networks generated by the method advanced herein are capable of learning new patterns as well as forgetting learned patterns without the necessity of recomputing all the interconnection weights and external inputs. The unlearning algorithm developed is then utilized to equalize the basins of attraction for each desired pattern to be stored in a given network, and to minimize the number of spurious states. Examples are given to illustrate the strengths and weaknesses of the methodologies