In this paper, a novel hybrid method based on learning algorithmof fuzzy neural network and Newton-Cotesmethods with positive coefficient for the solution of linear Fredholm integro-differential equation of the second kindwith fuzzy initial value is presented. Here neural network isconsidered as a part of large field called neural computing orsoft computing. We propose alearning algorithm from ...

This paper presents a novel method for unit commitment by synergistically combining Lagrangian relaxation for constraint handling with Hopfield-type recurrent neural networks for fast convergence to the minimum. The key idea is to set up a Hopfieldtype network using the negative dual as its energy function. This network is connected to “neuron-based dynamic programming modules” that make full u...

This paper presents a study on the performance of the Hopfield neural network algorithm for the graph path search problem. Specifically, performance of the Hopfield network is studied from the dynamic systems stability perspective. Simulations of the time behavior of the neural network is augmented with exhaustive stability analysis of the equilibrium points of the network dynamics. The goal is...

In this paper, an approach to solving the classical Traveling Salesman Problem (TSP) using a recurrent network of linear threshold (LT) neurons is proposed. It maps the classical TSP onto a single-layered recurrent neural network by embedding the constraints of the problem directly into the dynamics of the network. The proposed method differs from the classical Hopfield network in the update of...

In the present paper, an effort has been made for storing and recalling images with Hopfield Neural Network Model of auto-associative memory. Images are stored by calculating a corresponding weight matrix. Thereafter, starting from an arbitrary configuration, the memory will settle on exactly that stored image, which is nearest to the starting configuration in terms of Hamming distance. Thus gi...

Abstract— In this paper the Hopfield neural networks are adopted to solve the quadratic assignment problem, which is a generalization of the traveling salesman’s problem (TSP), the graph-partitioning problem (GPP), and the matching problem. When the Hopfield neural network was applied alone, a sub-optimal solution was obtained. By adding the 2exchange we obtained a solution very close to the op...

A model of neurons with hysteresis (or hysteresis binary neurons) for the Hopfield neural networks is studied. We prove theoretically that the emergent collective properties of the original Hopfield neural networks also are present in the Hopfield neural networks with hysteresis binary neurons. As an example, the networks are also applied to the maximum cut problem and results of computer simul...

Recently models of neural networks that can directly deal with complex numbers, complex-valued neural networks, have been proposed and several studies on their abilities of information processing have been done. In this paper, the problem of amplitude estimation of sinusoidal signals from observations corrupted by colored noise using Hopfield neural network (HNN) is considered. We have introduc...

The paper deals with a neural network controlled switch fabric with frame prioritization support. The impact of priority levels on the functionality and efficiency of this switch fabric was deeply investigated. The results of the studies related to the impact of the amount of priority levels are published in this paper. Key-Words: Hopfield neural network, switch fabric, prioritization, priority...

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...