In recent years, solar radiation forecasting has become highly important worldwide as energy increases its contribution to electricity grids. However, due the intermittent nature of caused by meteorological parameters, errors arise, and fluctuations in power output photovoltaic (PV) systems a severe issue. This paper aims introduce hybrid model daily global time series. Meteorological data samp...

This paper presents a new recurrent neural network (RNN) structure called ENEM for dynamic system identification. ENEM structure is based on Elman network and NARX neural network. In order to show the performance of ENEM for system identification, the results were also compared to the results of Elman network, Jordan network and their modified models. The identification results of linear and no...

Forecasting accuracy drives the performance of inventory management. This study is to investigate and compare different forecasting methods like Moving Average (MA) and Autoregressive Integrated Moving Average (ARIMA) with Neural Networks (NN) models as Feed-forward NN and Nonlinear Autoregressive network with eXogenous inputs (NARX). Data used to forecast is acquired from inventory database of...

Coronavirus (COVID-19) has captured the attention of globe very rapidly. Therefore, predicting spread disease become an indispensable process, this is being due to its extremely infectious nature and negative effects that some courses actions, which were taken minimize disease, have on economy key sectors (e.g., health, pharmaceutical industrial sectors). in research work, nonlinear autoregress...

This paper presents a methodology for identifying variable-structure models of magneto-rheological dampers (MRDs) that are structurally simple, easy to estimate and well suited for model-based control. Linear-in-the-parameters NARX models are adopted, and an identification method is developed based on the minimisation of the simulation error. Both the model structure and the parameters are sele...

This paper deals with the compensation of nonlinearities in dynamical systems using Nonlinear polynomial AutoRegressive models eXogenous inputs (NARX) identified from data. The approach is formulated for static and contexts general case also adapted hysteresis. Both simulated experimental results are presented to illustrate method. In case, proposed method compared other approaches was found be...