The consequences of two techniques for symmetrically diluting the weights of the standard Hopfield architecture associative memory model, trained using a non-Hebbian learning rule, are examined. This paper reports experimental investigations into the effect of dilution on factors such as: pattern stability and attractor performance. It is concluded that these networks maintain a reasonable leve...

We propose a method to define constraint weight parameters of the Hopfield network in order to establish the solutions of the optimization problem as stable equilibrium points in the state space. Application of the methodology is demonstrated on a well known benchmark problem, the Traveling Salesman Problem. Simulation results indicate that the proposed bounds on the constraint weight parameter...

A non symmetric version of Hopfield networks subject to state-multiplicative noise, pure time delay and Markov jumps is considered. Such networks seem to arise in the context of visuo-motor control loops and may, therefore, be used to mimic their complex behaviour. In this paper, we adopt the Lur’e-Postnikov systems approach to analyze the stochastic stability and the L2 gain of generalized Hop...

In this paper, we consider the global exponential stability of the equilibrium point of Hopfield neural networks with delays and impulsive perturbation. Some new exponential stability criteria of the system are derived by using the Lyapunov functional method and the linear matrix inequality approach for estimating the upper bound of the derivative of Lyapunov functional. Finally, we illustrate ...

An efficient technique that integrates the advantages of both fuzzy theory and Hopfield type neural network for object extraction from noisy background is proposed in this article. In the initial phase of the proposed technique, a fuzzy contrast enhancement of the input noisy object scene is carried out. Subsequently, the object scene is thresholded based on its fuzzy cardinality values to gene...

In this paper we look at a class of random optimization problems that arise in the forms typically known as Hopfield models. We view two scenarios which we term as the positive Hopfield form and the negative Hopfield form. For both of these scenarios we define the binary optimization problems that essentially emulate what would typically be known as the ground state energy of these models. We t...

Although the traditional permute matrix coming along with Hopfield is able to describe many common problems, it seems to have limitation in solving more complicated problem with more constrains, like resource leveling which is actually a NP problem. This paper tries to find a better solution for it by using neural network. In order to give the neural network description of resource leveling pro...

This paper presents a new genetic approach based on arithmetic crossover for solving the economic dispatch and environmentally constrained economic dispatch problems. Elitism, arithmetic crossover and mutation are used in the genetic algorithm to generate successive sets of possible operating policies. The proposed technique improves the quality of the solution. The employed arithmetic crossove...

This paper presents an improvement for an artificial neural network paradigm that has shown a significant potential for successful application to a class of optimization problems in structural engineering. The artificial neural network paradigm includes algorithms that belong to the class of single-layer, relaxationtype recurrent neural networks. The suggested improvement enhances the convergen...

After more than a decade of research, there now exist several neural-network techniques for solving NP-hard combinatorial optimization problems. Hopfield networks and selforganizing maps are the two main categories into which most of the approaches can be divided. Criticism of these approaches includes the tendency of the Hopfield network to produce infeasible solutions, and the lack of general...