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...

Memory plays a major role in Artificial Neural Networks. Without memory, Neural Network can not be learned itself. One of the primary concepts of memory in neural networks is Associative neural memories. A survey has been made on associative neural memories such as Simple associative memories (SAM), Dynamic associative memories (DAM), Bidirectional Associative memories (BAM), Hopfield memories,...

Quantum neural networks form one pillar of the emergent field quantum machine learning. Here generalizations classical realizing associative memories---capable retrieving patterns, or memories, from corrupted initial states---have been proposed. It is a challenging open problem to analyze memories with an extensive number patterns and determine maximal can reliably store, i.e., their storage ca...

Recently models of neural networks that can directly deal with complex numbers, complex-valued neural networks, have been proposed and several studies on their abilities of information processing have been done. In this paper, the problem of amplitude estimation of sinusoidal signals from observations corrupted by colored noise using Hopfield neural network (HNN) is considered. We have introduc...

Radio fuze needs to detect exactly target signal from the echo signal being polluted by noise in real time. Traditional interference cancellation system cannot meet the needs. The Hopfield neural network not only has the ability of nonlinear mapping but also has the ability of selflearning. So it can be used to possess a desired result against the effect of uncertainties and incomplete informat...

The Hopfield model is a pioneering neural network model with associative memory retrieval. The analytical solution of the model in mean field limit revealed that memories can be retrieved without any error up to a finite storage capacity of O(N), where N is the system size. Beyond the threshold, they are completely lost. Since the introduction of the Hopfield model, the theory of neural network...