We present a relaxation model for memory based on a generalized coulomb potential. The model has arbitrarily large storage capacity and, in addition, well-defined basins of attraction about stored memory states. The model is compared with the Hopfield relaxation model.

We introduce a novel type of neural network, termed the parallel Hopfield network, that can simultaneously effect the dynamics of many different, independent Hopfield networks in parallel in the same piece of neural hardware. Numerically we find that under certain conditions, each Hopfield subnetwork has a finite memory capacity approaching that of the equivalent isolated attractor network, whi...

In this paper, we propose a continuous hysteresis neurons (CHN) Hopfield neural network architecture for efficiently solving crossbar switch problems. A Hopfield neural network architecture with continuous hysteresis and its collective computational properties are studied. It is proved theoretically and confirmed by simulating the randomly generated Hopfield neural network with CHN. The network...

This paper introduces a new approach for neural network application to coastal studies. The method is based on the utilization of the Hopfield neural network to model sea surface current movements from single TiungSAT-1 image. In matching process using Hopfield neural network, identified features have to be mathematically compared to each other in order to build an energy function that will be ...

The two well-known neural network, Hopfield networks and Radial Basis Function networks, have different structures and characteristics. Hopfield neural network and RBF neural network are two of the most commonly-used types of feedback networks and feedforward networks respectively. This study gives an overview for Hopfield neural network and RBF neural network in architectures, the learning pro...

Due to feedback connections, recurrent neural networks (RNNs) are dynamic models. RNNs can provide more compact structure for approximating dynamic systems compared to feedforward neural networks (FNNs). For some RNN models such as the Hopfield model and the Boltzmann machine, the fixed-point property of the dynamic systems can be used for optimization and associative memory. The Hopfield model...

This paper describes an automated classifier for the identification of good wood and knotty wood from computer tomography (CT) images of logs. Such a system is intended to allow better assessment of saw logs before being cut into timber. We describe a new empirical model for the growth of Sitka Spruce (Picea Stichensis (Bong, Carr)) whose operation is adapted to Irish conditions. The use of Hop...