The complexity of helicopter flight dynamics makes modeling and helicopter system identification a very difficult task. Most of the traditional techniques require a model structure to be defined a priori and in case of helicopter dynamics, this is difficult due to its complexity and the interplay between various subsystems. To overcome this difficulty, non-parametric approaches are commonly ado...

In order to meet the new stringent environmental regulations, it is necessary to investigate the adaptive and optimal control strategies for the biological wastewater treatment processes. Nitrogen removal is one of the essential concerns in wastewater treatment. Nitrogen removal is a nonlinear, dynamic, and time variant complex process as complicated activities of microbial metabolism are invol...

Solar Radiation (SR) is one of the most important parameters in the design of photovoltaic systems (PVs). An accurate evaluation of the SR of a given location is essential for the efficient design and utilization of PVs. In this paper, a nonlinear autoregressive recurrent neural networks with exogenous input (NARX) was used to predict the SR in Mutah city. Hourly, weather data of three variable...

The applicability of approximate NARX models of non-linear dynamic systems is discussed. The models are obtained by a new version of Fourier analysis-based neural network also described in the paper. This constitutes a reformulation of a known method in a recursive manner, i.e. adapted to account for incoming data on-line. The method allows us to obtain an approximate model of the non-linear sy...

Abstract—This paper presents an optimization method for reducing the number of input channels and the complexity of the feed-forward NARX neural network (NN) without compromising the accuracy of the NN model. By utilizing the correlation analysis method, the most significant regressors are selected to form the input layer of the NN structure. An application of vehicle dynamic model identificati...

The Nonlinear autoregressive exogenous (NARX) model, which predicts the current value of a time series based upon its previous values as well as the current and past values of multiple driving (exogenous) series, has been studied for decades. Despite the fact that various NARX models have been developed, few of them can capture the long-term temporal dependencies appropriately and select the re...

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...

The HAMP linker, a predicted structural element observed in sensor proteins from all domains of life, is proposed to transmit signals between extracellular sensory input domains and cytoplasmic output domains. HAMP (histidine kinase, adenylyl cyclase, methyl-accepting chemotaxis protein, and phosphatase) linkers are located just inside the cytoplasmic membrane and are projected to form two shor...

A kernel method has been developed to model finite degree, finite memory length and infinite degree, finite memory length Volterra series using polynomial and exponential kernels, respectively. Here, the kernel method is extended to identify NARX (Nonlinear AutoRegressive with eXogenous inputs) models. To verify its effectiveness, the proposed approach is used in modeling friction dynamics, whi...

The power unit on board the ship generates periodic low-frequency vibration that affects normal operation of equipment board, and adaptive feedforward control algorithm can effectively suppress such harmful noise. But needs to obtain identification model secondary channels, frequency domain least squares method based linear Extended auto-regressive (ARX) is difficult with nonlinear characterist...