Outlining of boundaries of organs and tumors in CT and MRI images are prerequisite in medical applications. In this paper, a single layer Hopfield neural network called Contextual Hopfield Neural Network (CHNN) is presented for finding the edges of CT and MRI images. Different from the conventional 2-D Hopfield neural networks, the CHNN maps the two-dimensional Hopfield network at the original ...

During the recent decades, neural network models have been focused upon by researchers due to their more real performance and on this basis, different types of these models have been used in forecasting. Now, there is a question that which kind of these models has more explanatory power in forecasting the future processes of the stock. In line with this, the present paper made a comparison betw...

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 considers the use of discrete Hopfield neural networks for solving school timetabling problems. Two alternative formulations are provided for the problem: a standard Hopfield-Tank approach, and a more compact formulation which allows the Hopfield network to be competitive with swapping heuristics. It is demonstrated how these formulations can lead to different results. The Hopfield n...

Radio fuze needs to detect exactly target signal from the echo signal being polluted by noise in real time. Traditional interference cancellation system cannot meet the needs. The Hopfield neural network not only has the ability of nonlinear mapping but also has the ability of selflearning. So it can be used to possess a desired result against the effect of uncertainties and incomplete informat...

This paper suggests the systolic array implementation of block based Hopfield neural network architecture using completely digital circuits. The design is based on rewriting the energy equation of Hopfield neural network to a systolic (or modular) form. The performance of the proposed architecture is evaluated by applying various binary inputs and it is observed that the network provides massiv...

A neural network which is recognized as artificial neural network is a mathematical model or computational model that tries to simulate the structure and functional aspect of biological neural networks. It can solve complicated recognition solve optimization problems and analysis problems. It is because it composed of huge amount of interconnected neurons to solve specific problems [1]. Hopfiel...