This paper deals with the compensation of nonlinearities in dynamical systems using Nonlinear polynomial AutoRegressive models eXogenous inputs (NARX) identified from data. The approach is formulated for static and contexts general case also adapted hysteresis. Both simulated experimental results are presented to illustrate method. In case, proposed method compared other approaches was found be...

Regressor selection can be viewed as the rst step in the system identi cation process. The bene ts of nding good regressors before estimating complex models are especially clear for nonlinear systems, where the class of possible models is huge. In this article, a structured way of using the tool Analysis of Variance (ANOVA) is presented and used for NARX model (nonlinear autoregressive model wi...

Aiming at engine health management, a novel hybrid prediction method is proposed for exhaust gas temperature (EGT) of turbine engines. This model combines nonlinear autoregressive with exogenous input (NARX) and moving average (MA) model. A feature attention mechanism-enhanced long short-term memory network (FAE-LSTM) first developed to construct the NARX model, which used identifying aircraft ...

In order to improve the accuracy of power load forecasting, this paper proposes a neural network-based short-term monitoring method. First, original energy signal is decomposed by CEEMDAN algorithm obtain several eigenmode function components and residual components; functions are fed into NARX network for computational purposes. The partial hypothesis superimposed in following part final forec...

In this paper we study nonlinear ARX models in relation to a class of kernel based models which make use of kernel induced feature spaces, a methodology which is common in the area of support vector machines (SVMs). Methods are proposed for extending the use of least squares support vector machine (LS-SVM) models towards a robust setting. In order to understand the robustness of these estimator...

Battery and energy management methodologies have been proposed to address the design challenges of driving range and battery lifetime in Electric Vehicles (EV). However, the driving behavior is a major factor which has been neglected in these methodologies. In this paper, we propose a novel context-aware methodology to estimate the driving behavior in terms of future vehicle speeds and integrat...

Reservoir computing (RC) is a novel approach to time series prediction using recurrent neural networks. In RC, an input signal perturbs the intrinsic dynamics of a medium called a reservoir. A readout layer is then trained to reconstruct a target output from the reservoir’s state. The multitude of RC architectures and evaluation metrics poses a challenge to both practitioners and theorists who ...