A neural hybrid system for large memory association

Abstract

A neural hybrid system based on Kohonen and Hopfield networks is proposed for memory association. It uses a heuristic approach to split a total set of patterns into various subsets with the aim to increase performance of the parallel architecture of Hopfield networks (PAHN). This architecture avoids several spurious states enabling a pattern storage capacity larger then permitted by a typical Hopfield network. The strategy consists of a method to sort patterns with the SOM algorithm and distribute them into these subsets in such a way that the patterns of the same subset are to be as more orthogonal as possible among themselves. The results show that the strategy employed to distribute patterns in subsets works well when compared with the random distributions and with the exhaustive approach. The results also show that the proposed heuristic lead to patterns subsets that enable more robust memory retrieval.