The combination of evolutionary algorithms and ANN has been a recent interest in the field of research. Hopfield model is a type of recurrent neural network which has been widely studied for the purpose of associative memories. In the present work, this Hopfield Model of feedback neural networks has been studied with Monte Carlo adaptation learning rule and one evolutionary searching algorithm ...

In this paper, a decentralized method for Load Balancing in IEEE 802.11 wireless LANs is proposed. In this proposed method, users autonomously select the most appropriate Access Point (AP) by sensing available APs, and selecting the best one. The Hopfield Neural Networks are used which is an autonomous and decentralized optimization technique. The Hopfield Neural Networks can be used in optimiz...

In this paper, we present FPGA recurrent neural network systems with learning capability using the simultaneous perturbation learning rule. In the neural network systems, outputs and internal values are represented by pulse train. That is, analog recurrent neural networks with pulse frequency representation are considered. The pulse density representation and the simultaneous perturbation enabl...

It is known that an analog Hopfield neural network with time delay can generate the outputs which are similar to the human electroencephalogram. To gain deeper insights into the mechanisms of rhythm generation by the Hopfield neural networks and to study the effects of noise on their activities, we investigated the behaviors of the networks with symmetric and asymmetric interneuron connections....

A Hopfield network is an auto-associative, distributive model of neural memory storage and retrieval. A form of error-correcting code, the Hopfield network can learn a set of patterns as stable points of the network dynamic, and retrieve them from noisy inputs – thus Hopfield networks are their own decoders. Unlike in coding theory, where the information rate of a good code (in the Shannon sens...

We introduce a novel type of neural network, termed the parallel Hopfield network, that can simultaneously effect the dynamics of many different, independent Hopfield networks in parallel in the same piece of neural hardware. Numerically we find that under certain conditions, each Hopfield subnetwork has a finite memory capacity approaching that of the equivalent isolated attractor network, whi...

the main objective of this paper is to introduce a new intelligent optimization technique that uses a predictioncorrectionstrategy supported by a recurrent neural network for finding a near optimal solution of a givenobjective function. recently there have been attempts for using artificial neural networks (anns) in optimizationproblems and some types of anns such as hopfield network and boltzm...

Hopfield neural networks and interior point methods are used in an integrated way to solve linear optimization problems. The Hopfield network gives warm start for the primal–dual interior point methods, which can be way ahead in the path to optimality. The approaches were applied to a set of real world linear programming problems. The integrated approaches provide promising results, indicating ...

Recently neural networks have been ploposed as new computational tools for solving constrained optimization problems. In this paper the minimum cost path fmding algorithm is proposed by using a Hopfield type neural network. In order to design a Hopfield type neural network, an energy function must be defmed at f i t . To achieve thii, the concept of a vector-represented network is used to descr...