The paper discusses the applicability of approximate NARX models of nonlinear dynamic systems to model based nonlinear control. The models might be obtained by a new version of Fourier analysis based neural network. The proposed controller is based on a discrete-time model of the plant. The objective is to incorporate plant modelling and control design into a unified framework where on the one ...

The growing proliferation in solar deployment, especially at distribution level, has made the case for power system operators to develop more accurate solar forecasting models. This paper proposes a solar photovoltaic (PV) generation forecasting model based on multi-level solar measurements and utilizing a nonlinear autoregressive with exogenous input (NARX) model to improve the training and ac...

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...

We introduce GP-RLARX, a novel Gaussian Process (GP) model for robust system identification. Our approach draws inspiration from nonlinear autoregressive modeling with exogenous inputs (NARX) and it encapsulates a novel and powerful structure referred to as latent autoregression. This structure accounts for the feedback of uncertain values during training and provides a natural framework for fr...

In our work, we compared two approaches for predicting changes in the concentration of one main greenhouse gases - methane. The study is based on surface methane data obtained by monitoring dynamics major Arctic Island Belyy, Russia. We used a nonlinear autoregressive neural network with an external input (NARX), and vector regression model. An artificial type NARX was more accurate changes.

This paper describes a multi-step algorithm used to predict and typify the energy consumption profile of prosumer, allowing automation design self-consumption photovoltaic (PV) power systems in novel platform called PV SPREAD. The uses different methodologies address various possible scenarios data availability. In this paper, those are addressed using nonlinear autoregressive artificial neural...

In this paper we propose a new technique for the identification of NARX Hammerstein systems. The new technique is based on the theory of Least Squares Support Vector Machines function-approximation and allows to determine the memoryless static nonlinearity as well as the linear model parameters. As the technique is non-parametric by nature, no assumptions about the static nonlinearity need to b...

A procedure for the selection of neural models of dynamical processes is presented. It uses statistical tests at various levels of model reduction, in order to provide optimal tradeoffs between accuracy and parsimony. The efficiency of the method is illustrated by the modeling of a highly non-linear NARX process.

October December 2016 2677 JCPS Volume 9 Issue 4 Inverse Kinematics Solution of a Five Joint Robot Using NARX Algorithm I. Jacob Raglend, M. Dev Anand, G. Glan Devadhas, D. M. Mary Synthia Regis Prabha Department of Electrical and Electronics Engineering, Noorul Islam Centre for Higher Education, Kumaracoil 629 180, Thuckalay, Kanyakumari District, Tamilnadu State, India Department of Mechanica...