Hetero associative neural network for pattern recognition

An inter-pattern association (IPA) neural network model is presented in which basic logical operations are used to determine the association among common and special features of reference patterns. Hetero- and auto-associative memory are synthesized by applying a generalized logical rule. Computer simulations for pattern recognition by using the IPA model have shown a better performance and a higher storage capacity than the Hopfield model. A 2-D adaptive optical neural network is used to perform parallel neurocomputations. Since the interconnection weight matrix for the IPA model has a tristate structure, the dynamic range imposed on a spatial light modulator is rather relaxed, and the interconnections are much simpler than for the Hopfield model.<>