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...

This paper describes the application of an unsupervised parallel approach called the Annealed Hopfield Neural Network (AHNN) using a modified cost function with moment and entropy preservation for magnetic resonance image (MRI) classification. In the AHNN, the neural network architecture is same as the original 2-D Hopfield net. And a new cooling schedule is embedded in order to make the modifi...

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 ...

So far we have been studying networks that only have forward connections and thus there have been no loops. Networks with loops — recurrent connections is the technical term used in the literature — have an internal dynamics. The networks that we have introduced so far have only retained information about the past in terms of their weights, which have been changed according to the learning rule...