In this paper, we investigated the fixed-time synchronization problem of a type reaction-diffusion fuzzy neural networks with stochastic perturbations by developing simple control schemes. First, some generalized stability results are introduced for nonlinear systems. Based on these results, generic criteria established and upper bounds settling time directly calculated using several special fu...

The paper presents a comparison of various soft computing techniques used for filtering and enhancing speech signals. The three major techniques that fall under soft computing are neural networks, fuzzy systems and genetic algorithms. Other hybrid techniques such as neuro-fuzzy systems are also available. In general, soft computing techniques have been experimentally observed to give far superi...

<p style='text-indent:20px;'>In this paper, we investigate a class of stochastic recurrent neural networks with discrete and distributed delays for both biological mathematical interests. We do not assume any Lipschitz condition on the nonlinear term, just continuity assumption together growth conditions so that uniqueness Cauchy problem fails to be true. Moreover, existence pullback attr...

In this paper we solve a wide rang of Semidefinite Programming (SDP) Problem by using Recurrent Neural Networks (RNNs). SDP is an important numerical tool for analysis and synthesis in systems and control theory. First we reformulate the problem to a linear programming problem, second we reformulate it to a first order system of ordinary differential equations. Then a recurrent neural network...

The feed-forward multilayer networks (perceptrons, radial basis function networks (RBF), probabilistic networks, etc.) are currently used as „static systems“ in pattern recognition, speech generation, identification and control, prediction, etc. (see, e. g. [1]). Theoretical works by several researchers, including [2] and [3] have proved that, even with one hidden layer, a perceptron neural net...

A reliable multi-step predictor is very useful to a wide array of applications to forecast the behavior of dynamic systems. The objective of this paper is to develop a more robust data-driven predictor for time series forecasting. Based on simulation analysis, it is found that multi-step-ahead forecasting schemes based on step inputs perform better than those based on sequential inputs. It is a...

Recurrent neural network is a powerful model that learns temporal patterns in sequential data. For a long time, it was believed that recurrent networks are difficult to train using simple optimizers, such as stochastic gradient descent, due to the so-called vanishing gradient problem. In this paper, we show that learning longer term patterns in real data, such as in natural language, is perfect...