Enhanced Memory Capacity Of A Hopfield Neural Network

The performance of an associative memory based on the Hopfield Model of a neural network is data dependent. When programmed memories are too similar (a small Hamming distance between memories) the associative memory system is easily confused; settling either to incorrect or in some cases, undefined states. This paper describes a series of computer simulations performed on a 100-node Hopfield network. The programs were written in the APL language, which is highly efficient for this type of system. The simulations examined the sources of confusion and led to a preprocessing approach which substantially reduces the confusion. The simulations were also extended in the direction of coupling several small neural networks to form one integrated low-confusion associative memory. The coupling of the neural subnetworks was through a voting scheme wherein each node of a subnetwork consulted the analogous node of the other subnetworks; the decision to change state or remain the same is based on majority rule. The performance of these two associative memory systems is detailed and compared to a conventional Hopfield system.