Least squares associative memory and a theoretical comparison of its performance

Abstract

A novel associative memory model is proposed in which both the stable and optimal (minimum error) association are realized even when noisy input key vectors are given. The performance of the model is compared with that of typical associative memory models (Kohonen's model and the stabilized model) theoretically and experimentally (by computer simulation). The proposed model takes full consideration of the input noise conditions (magnitude of noise). Since the relationship between the proposed model and Kohonen's model is similar to that between the Wiener filter and the inverse filter, the proposed associative memory model is more practical than Kohonen's model. >