In this paper, we propose a continuous hysteresis neurons Hopfield neural network architecture for efficiently solving crossbar switch problems. A Hopfield neural network architecture with continuous hysteresis and its collective computational properties are studied. It is proved theoretically and confirmed by simulating the randomly generated Hopfield neural network with continuous hysteresis ...

This paper serves as a tutorial on the use of neural networks for solving combinatorial optimization problems. It reviews the two main classes of neural network models: the gradientbased neural networks such as the Hopfield network, and the deformable template approaches such as the elastic net method and self-organizing maps. In each class, the original model is presented, its limitations disc...

ABSTRACT In the present paper, an effort has been made to compare and analyze the performance for pattern recalling with conventional hebbian learning rule and with evolutionary algorithm in Hopfield Model of feedback Neural Networks. A set of ten objects has been considered as the pattern set. In the Hopfield type of neural networks of associative memory, the weighted code of input patterns pr...

In this paper a new design procedure for Hopfield associative memories storing grey-scale images is presented. The proposed architecture, with both intra-layer and inter-layer connections, is an evolution of a previous work based on the decomposition of the image with 2L gray levels into L binary patterns, stored in L uncoupled neural networks: that allows to store images with the commonly used...

Cohen-Grossberg neural networks which include Hopfield neural networks and Cellular neural networks as special cases and have important applications in many fields are researched by many authors. There is few results on synchronization control of delayed Cohen-Grossberg neural networks. In this paper, the synchronization control of delayed CohenGrossberg neural networks with diffusion terms is ...

2 Neural Nets, Statistical Physics and Optimization It has recently been shown that neural net models can be based upon the principles of statistical physics, In particular the spin sociative memories and optimization networks. The simulation of such neural networks promises to provide valuable insights into human and machine vision. However, for a large amay, the calculations involved become p...

This paper can be regarded as the continuation of the work of the authors contained in papers (2015). At the same time, it represents the extension of the papers Lou and Cui (2007, [24]), Sannay (2007, [34]) and Acka et al. (2004, [1]). This work discusses a generalized model of high-order Hopfield-type neural networks with time-varying delays. By utilizing Lyapunov functional method and the li...

The paper introduces a general class of neural networks where the neuron activations are modeled by discontinuous functions. The neural networks have an additive interconnecting structure and they include as particular cases the Hopfield neural networks (HNNs), and the standard Cellular Neural Networks (CNNs), in the limiting situation where the HNNs and CNNs possess neurons with infinite gain....