Nonlinear autoregressive moving average with exogenous inputs (NARMAX) models have been successfully demonstrated for modeling the input-output behavior of many complex systems. This paper deals with the proposition of a scheme to provide time series prediction. The approach is based on a recurrent NARX model obtained by linear combination of a recurrent neural network (RNN) output and the real...

The problem of chaotic time series prediction is studied in various disciplines now including engineering, medical and econometric applications. Chaotic time series are the output of a deterministic system with positive Liapunov exponent. A time series prediction is a suitable application for a neuronal network predictor. The NN approach to time series prediction is non-parametric, in the sense...

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...