This paper deals with the global asymptotic stability problem for Hopfield neural networks with time-varying delays. By resorting to the integral inequality and constructing a Lyapunov-Krasovskii functional, a novel delay-dependent condition is established to guarantee the existence and global asymptotic stability of the unique equilibrium point for a given delayed Hopfield neural network. This...

In this paper, an unsupervised parallel segmentation approach using a fuzzy Hopfield neural network (FHNN) is proposed. The main purpose is to embed fuzzy clustering into neural networks so that on-line learning and parallel implementation for medical image segmentation are feasible. The idea is to cast a clustering problem as a minimization problem where the criteria for the optimum segmentati...

2014 We study pattern recognition in linear Hopfield type networks of N neurons where each neuron is connected to the z subsequent neurons such that the state of the ith neuron at time t + 1 is determined by the states of neurons i + 1, ...,i + z at time t. We find that for small values of z/N the retrieval behavior differs considerably from the behavior of diluted Hopfield networks. The maximu...

in this paper, we show how to obtain suitable differential charactristics for block ciphers with neural networks. we represent the operations of a block cipher, regarding their differential characteristics, through a directed weighted graph. in this way, the problem of finding the best differential characteristic for a block cipher reduces to the problem of finding the minimum-weight multi-path...

Hopfield Neural Networks (HNNs) are recurrent neural networks used to implement associative memory. They can be applied pattern recognition, optimization, or image segmentation. However, sometimes it is not easy provide the users with good explanations about results obtained them due mainly large number of changes in state neurons (and their weights) produced during a problem machine learning. ...

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...