in the near future the use of distributed generation systems will play a big role in the production ofelectrical energy. one of the most common types of dg technologies , fuel cells , which can be connectedto the national grid by power electronic converters or work alone studies the dynamic behavior andstability of the power grid is of crucial importance. these studies need to know the exact mo...

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<p style='text-indent:20px;'>We consider a class of neutral type Clifford-valued cellular neural networks with discrete delays and infinitely distributed delays. Unlike most previous studies on networks, we assume that the self feedback connection weights are Clifford numbers rather than real numbers. In order to study existence <inline-formula><tex-math id="M1">\begin{documen...

A multilayer neural network based on multi-valued neurons (MLMVN) is a new powerful tool for solving classification, recognition and prediction problems. This network has a number of specific properties and advantages that follow from the nature of a multi-valued neuron (complexvalued weights and inputs/outputs lying on the unit circle). Its backpropagation learning algorithm is derivative-free...

In this paper, the adaptive synchronization of fractional-order complex-valued neural networks with time-varying delays (FOCVNNTDs) is investigated. First, two novel differential inequalities time are established, which can be seen as an extension Halanay inequality. Besides, complete and quasi-projective FOCVNNTDs investigated based on using a controller. addition, instead separating into real...

artificial neural networks are used in many smart apparatus and different fields such as signal processing pattern diagnoses, military systems, medicine, financial systems, and artificial intelligence. in this article using quality of neural networks in optimizing energy cost in moving limb and its effectiveness in organization a cognitive function founded by presenting an algorithm for use in ...

We review recent results about the maximal values of the Kullback-Leibler information divergence from statistical models defined by neural networks, including näıve Bayes models, restricted Boltzmann machines, deep belief networks, and various classes of exponential families. We illustrate approaches to compute the maximal divergence from a given model starting from simple subor super-models. W...

In this paper, we consider the edge detection problem using an intelligent approach. We use a multilayer neural network based on multi-valued neurons (MLMVN) as an intelligent edge enhancer. MLMVN is a complex-valued neural network and it has many advantages over classical neural networks. It significantly outperforms a classical multilayer feedforward neural network in terms of learning speed,...

In this work, a recursive Levenberg-Marquardt (LM) learning algorithm in the complex domain is developed and applied to the learning of an adaptive control scheme composed by ComplexValued Recurrent Neural Networks (CVRNN). We simplified the derivation of the LM learning algorithm using a diagrammatic method to derive the adjoint CVRNN used to obtain the gradient terms. Furthermore, we apply th...