An Expanding Construction of Neural Networks Improving Associative Ability

Abstract

In this paper, we propose an expanding construction of neural networks to improve associative ability when we use the projection rule to memorize prototype vectors in the networks. In order to embed the prototype vectors in the high order networks, we add an arbitrary fixed pattern to each prototype vector at the memorizing process and add it to each key vector at the recalling process. The associative ability is concerned with the domain of attraction of each equilibrium corresponding to each prototype vector. We evaluate the domain of attraction of the networks and prove that the domain of attraction of the expanded networks is larger than that of the non-expanded networks. The evaluation of the domain of attraction depends only on the order of added pattern. The simulation results show the quantitative relations between the order of the networks and the domain of attraction.