Load modeling is widely used in power system studies. Two types of modeling, namely, static and dynamic, are employed. The current industrial practice is the static modeling. Static modelss are algebraic equations of active and reactive power changes in terms of voltage and frequency deviations. In this paper, a component based on static modeling is employed in which the aggregate model is deri...

Load modeling is widely used in power system studies. Two types of modeling, namely, static and dynamic, are employed. The current industrial practice is the static modeling. Static modelss are algebraic equations of active and reactive power changes in terms of voltage and frequency deviations. In this paper, a component based on static modeling is employed in which the aggregate model is deri...

load modeling is widely used in power system studies. two types of modeling, namely, static and dynamic, are employed. the current industrial practice is the static modeling. static modelss are algebraic equations of active and reactive power changes in terms of voltage and frequency deviations. in this paper, a component based on static modeling is employed in which the aggregate model is deri...

regeneration and engineering of functional new tissues containing the neural network have great importance. progression of neural network into the dental tissue has a crucial role in dental tissue regeneration. in the present study polymer-ceramic blended scaffolds containing different weight percentages of carbon nanotube in poly caprolactone nanofiber matrix were fabricated. morphological, me...

In this paper, the nonlinear autoregressive model with exogenous variables as a new neural network is used for timing of the stock markets on the basis of the technical analysis of Japanese Candlestick. In this model, the “nonlinear autoregressive model with exogenous variables” is an analyzer. For a more reliable comparison, here (like the literature) two approaches of  Raw-based and Signal-ba...

neural networks are applicable in identification systems from input-output data. in this report, we analyze thehammerstein-wiener models and identify them. thehammerstein-wiener systems are the simplest type of block orientednonlinear systems where the linear dynamic block issandwiched in between two static nonlinear blocks, whichappear in many engineering applications; the aim of nonlinearsyst...

The concept of action recognition in smart security heavily relies on deep learning and artificial intelligence to make predictions about actions humans. To draw appropriate conclusions from these hypotheses, a large amount information is required. data question are often video feed, there direct relationship between increased volume more-precise decision-making. We seek determine how far stati...

A systematic comparison of two basic types of neural network, static and dynamic, is presented in this study. Two backpropagation (BP) learning optimization algorithms, the standard BP and conjugate gradient (CG) method, are used for the static network, and the real-time recurrent learning (RTRL) algorithm is used for the dynamic-feedback network. Twenty-three storm-events, about 1632 rainfall ...