This paper presents a novel hybrid algorithm for combinatorial optimization problems based on mixing the cross-entropy (CE) method and a Hopfield neural network. The algorithm uses the CE method as a global search procedure, whereas the Hopfield network is used to solve the constraints associated to the problems. We have shown the validity of our approach in several instance of the generalized ...

In this paper two different approaches to solve Sudoku puzzles with neural networks are presented. The first approach is proposed by J.J. Hopfield. He tries to solve the Sudoku puzzle with help of a Hopfield network and treated the problem as an integer optimization problem that is also used for the solution of the well known Traveling Salesmen Problem (TSP). Second solution uses the Hopfield n...

In this paper, we propose a continuous hysteresis neurons (CHN) 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 CHN. The network...

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 ...

After the original work of Hopfield and Tank, a lot of modified Hopfield neural network models have been proposed for combinatorial optimization problems. Recently, a positively selffeedbacked Hopfield neural network architecture was proposed by Li et al. and successfully applied to crossbar switching problem. In this paper, we analysis the dynamics of the positively self-feedbacked Hopfield ne...

estimation (forecasting) of industrial production costs is one of the most important factor affecting decisions in the highly competitive markets. thus, accuracy of the estimation is highly desirable. hibrid regression neural network is an approach proposed in this paper to obtain better fitness in comparison with regression analysis and the neural network methods. comparing the estimated resul...