This study aims to determine an automatic forecasting method of univariate time series, using the nonlinear autoregressive neural network model with exogenous input (NARX). In this setting, users only need supply series. Then, algorithm sets up appropriate features, estimate parameters in model, and calculate forecasts, without users’ intervention. The used include preprocessing, tests for tren...

One of the issues associated with supply electricity is its generation capacity, and this has led to prevalent power cuts high costs usage experienced in many developing nations, including South Africa. Historical research shown that annual rate increase for grown at an alarming since 2008 and, some years, as much 16%. The objectives study are estimate cost analysis twenty-nine residences Unive...

Acute Respiratory Distress Syndrome (ARDS) is a major cause of morbidity and has a high rate of mortality. ARDS patients in the intensive care unit (ICU) require mechanical ventilation (MV) for breathing support, but inappropriate settings of MV can lead to ventilator induced lung injury (VILI). Those complications may be avoided by carefully optimizing ventilation parameters through model-base...

The nonlinear and dynamic accommodating capability of time domain models makes them a useful representation of chaotic time series for analysis, modeling and prediction. This paper is devoted to the modeling and prediction of chaotic time series with hidden attractors using a nonlinear autoregressive model with exogenous inputs (NARX) based on a novel recurrent fuzzy functions (RFFs) approach. ...

This paper proposes an effective Photovoltaic (PV) Power Forecasting (PVPF) technique based on hierarchical learning combining Nonlinear Auto-Regressive Neural Networks with exogenous input (NARXNN) Long Short-Term Memory (LSTM) model. First, the NARXNN model acquires data to generate a residual error vector. Then, stacked LSTM model, optimized by Tabu search algorithm, uses correction associat...

A novel prediction scheme is proposed for real-time MPEG video to predict the burst and long-range dependent traffic. The trend and periodic characteristics of MPEG video traffic are fully captured by a proposed stochastic state-space dynamic model. Then a recursive filtering algorithm is proposed to estimate traffic for long-range prediction. Simulation results based on real MPEG traffic data ...

This paper introduces a novel ensemble learning approach based on recurrent radial basis function networks (RRBFN) for time series prediction with the aim of increasing the prediction accuracy. Standing for the base learner in this ensemble, the adaptive recurrent network proposed is based on the nonlinear autoregressive with exogenous input model (NARX) and works according to a multi-step (MS)...

Histidine kinase receptors are a large family of membrane-spanning proteins found in many prokaryotes and some eukaryotes. They are a part of two-component signal transduction systems, which each comprise a sensor kinase and a response regulator and are involved with the regulation of many cellular processes. NarX is a histidine kinase receptor that responds to nitrate and nitrite to effect reg...