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...

A new unified modelling framework based on the superposition of additive submodels, functional components, and wavelet decompositions is proposed for nonlinear system identification. A nonlinear model, which is often represented using a multivariate nonlinear function, is initially decomposed into a number of functional components via the well known analysis of variance (ANOVA) expression, whic...

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...