A multimodule neural network model and the estimate of its nature as associative memory

Abstract

AbstractA modular neural network model, “Cross‐Coupled Hopfield Nets with Many‐to‐Many Mapping Internetworks (CCHN‐MMMI),” in which the total consumed energy is defined as a linear sum of energy functions for subnetworks and their interactions, is proposed herein. This model can be applied to cases in which many‐to‐many mappings are necessary to mutually relate states of modules. The network dynamics are derived first, and then through computer simulations, the characteristics of the 2‐module CCHN‐MMMI are studied as associative memory. In one simulation, the effects of the introduction of explicitly modular architecture and a comparison of the results to those produced by conventional autocorrelation associative memory models.Another simulation shows that the model can store and recall pairs of character patterns successfully even if many‐to‐many mapping relations are necessary to relate the pairs to each other. A quantitative investigation as to how the recall dynamics deteriorate with an increasing number of randomly chosen fundamental memories is made in order to access the model as associative. The results indicate that internetworks between modules function effectively to prevent the entire network from recalling spurious memories. When multilayered networks with nonlinear hidden units are employed as internetworks in the model, the association performance is even more greatly improved as compared with conventional autocorrelation associative memory model.