â  abstract: in this paper, artificial neural network (ann) was used for modeling the nonlinear structure of a debutanizer column in a refinery gas process plant. the actual input-output data of the system were measured in order to be used for system identification based on root mean square error (rmse) minimization approach. it was shown that the designed recurrent neural network is able to pr...

System identification uses measurements of a dynamic system's input and output to reconstruct mathematical model for that system. These can be mechanical, electrical, physiological, among others. Since most the systems around us exhibit some form nonlinear behavior, system techniques are tools will help gain better understanding our surroundings potentially let improve their performance. One is...

The prevalence of batch and batch-like operations, in conjunction with the continued resurgence artificial intelligence techniques for clustering classification applications, has increasingly motivated exploration applicability deep learning modeling feedback control processes. To this end, present study seeks to evaluate viability general, neural networks particular, toward process via a case ...

System identification (SI) is the discipline of inferring mathematical models from unknown dynamic systems using input/output observations such with or without prior knowledge some system parameters. Many valid algorithms are available in literature, including Volterra series expansion, Hammerstein–Wiener models, nonlinear auto-regressive moving average model exogenous inputs (NARMAX) and its d...

We present a reconstruction of the dynamics radiation belts from solar cycles 17 to 24 which allows us study how belt activity has varied between different cycles. The simulations are produced using Versatile Electron Radiation Belt (VERB)-3D code. VERB-3D code incorporate radial, energy, and pitch angle diffusion reproduce belts. Our use historical measurements Kp (available since cycle 17, i....

The ability of Escherichia coli to use both nitrate and nitrite as terminal electron acceptors during anaerobic growth is mediated by the dual-acting two-component regulatory systems NarX-NarL and NarQ-NarP. In contrast, Neisseria gonorrhoeae responds only to nitrite: it expresses only NarQ-NarP. We have shown that although N. gonorrhoeae NarQ can phosphorylate E. coli NarL and NarP, the N. gon...

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 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. ...

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...