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...

Fractionation product properties of crude distillation unit (CDU) need to be monitored and controlled through feedback mechanism. Due to inability of on-line measurement, soft sensors for product quality estimation are developed. Soft sensors for kerosene distillation end point are developed using linear and nonlinear identification methods. Experimental data are acquired from the refinery dist...

A full-fledged neural network modeling, based on a Multi-layered Nonlinear Autoregressive Exogenous Neural Network (NARX) architecture, is proposed for quasi-static and dynamic hysteresis loops, one of the most challenging topics computational magnetism. This modeling approach overcomes drawbacks in attaining better than percent-level accuracy classical recent approaches accelerator magnets, th...

Abstract. It is now well established to use shallow artificial neural networks (ANNs) obtain accurate and reliable groundwater level forecasts, which are an important tool for sustainable management. However, we observe increasing shift from conventional ANNs state-of-the-art deep-learning (DL) techniques, but a direct comparison of the performance often lacking. Although they have already clea...

The extreme values of high tides are generally caused by a combination astronomical and meteorological causes, as well the conformation sea basin. One place where tide have considerable practical interest is city Venice. MOSE (MOdulo Sperimentale Elettromeccanico) system was created to protect Venice from flooding highest tides. Proper operation protection requires an adequate forecast model ti...

A Markov chain approach to identification of the Wiener, Hammerstein, and nonlinear ARX (NARX) systems is presented. The motivation of this approach comes from the fact that these classes of nonlinear systems are connected with Markov chains, and hence their asymptotical properties, such as ergodicity, stationarity, and invariant probability distribution, can be derived from the corresponding c...

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...

Simple Recurrent Networks (SRNs) have been widely used in natural language processing tasks. However, their ability to handle long-term dependencies between sentence constituents is somewhat limited. NARX networks have recently been shown to outperform SRNs by preserving past information in explicit delays from the network’s prior output. However, it is unclear how the number of delays should b...

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...