A multiscale method is described in the context of binary Hopfield-type neural networks. The appropriateness of the proposed technique for solving several classes of optimization problems is established by means of the notion of group update which is introduced here and investigated in relation to the properties of multiscaling. The method has been tested in the solution of partitioning and cov...

The addition of noise to the deterministic Hopfield network, trained with one shot Hebbian learning, is known to bring benefits in the elimination of spurious attractors. This paper extends the analysis to learning rules that have a much higher capacity. The relative energy of desired and spurious attractors is reported and the affect of adding noise to the dynamics is empirically investigated....

We are interested in finding near-optimal solutions to hard optimization problems using Hopfield-type neural networks. The methodology is based on a basic property of such networks, that of reducing their ‘energy’ during evolution, leading to a local or global minimum. The methodology is presented and several different network models usually employed as optimizers (Analog Hopfield net with Simu...

This paper presents an artificial neural network system using Hopfield and Kosko associative memory algorithms. Both of algorithms are used for recognising patterns from partial/noisy input or from other associated patterns. The recognition rate of the system has been effectively improved by adjusting the network’s parameters and by modifying the associative memory function for converging to a ...

The consequences of imposing a sign constraint on the standard Hopfield architecture associative memory model, trained using perceptron like learning rules, is examined. Such learning rules have been shown to have capacity of at most half of their unconstrained versions. This paper reports experimental investigations into the consequences of constraining the sign of the network weights in terms...

This paper look at how the Hopfield neural network can be used to store and recall patterns constructed from natural language sentences. As a pattern recognition and storage tool, the Hopfield neural network has received much attention. This attention however has been mainly in the field of statistical physics due to the model’s simple abstraction of spin glass systems. A discussion is made of ...

The Little-Hopfield network is an auto-associative computational model of neural memory storage and retrieval. This model is known to robustly store collections of randomly generated binary patterns as stable-states of the network dynamics. However, the number of binary memories so storable scales linearly in the number of neurons, and it has been a long-standing open problem whether robust exp...

This work aims at reviewing some of the main issues that are under research in the field of Hopfield networks. In particular, the feasibility of the Hopfield network as a practical optimization method is addressed. Together with the current results, the main directions that deserve ongoing analysis are shown. Besides, some suggestions are provided in order to identify lines that are at an impas...

Networks of coupled periodic oscillators (similar to the Kuramoto model) have been proposed as models of associative memory. However, error-free retrieval states of such oscillatory networks are typically unstable, resulting in a near zero capacity. This puts the networks at disadvantage as compared with the classical Hopfield network. Here we propose a simple remedy for this undesirable proper...

Author Affiliation: Department of Electrical and Electronic Engineering, Nigde University, Nigde, Turkey. Abstract: This letter outlines a hybrid genetic algorithm (HGA) for solving the economic dispatch problem. The algorithm incorporates the solution produced by an improved Hopfield neural network (NN) [1] as a part of its initial population. Elitism, arithmetic crossover, and mutation are us...