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...

BACKGROUND For mechanically ventilated patients with acute respiratory distress syndrome (ARDS), suboptimal PEEP levels can cause ventilator induced lung injury (VILI). In particular, high PEEP and high peak inspiratory pressures (PIP) can cause over distension of alveoli that is associated with VILI. However, PEEP must also be sufficient to maintain recruitment in ARDS lungs. A lung model that...

Rainfall prediction is one of the most challenging task faced by researchers over years. Many machine learning and AI based algorithms have been implemented on different datasets for better purposes, but there not a single solution which perfectly predicts rainfall. Accurate still remains question to researchers. We offer learning-based comparison evaluation rainfall models Kashmir province. Bo...

Freshwater supply is a major challenge in regions with limited water resources and extremely arid climatic conditions. The objective of this study to model the monthly demand Kuwait using nonlinear autoregressive exogenous input (NARX) neural network approach. country lacks conventional surface characterized by climate. In addition, it has one fastest growing populations. study, linear detrendi...

Abstract Nonlinear Auto-Regressive eXogenous input (NARX) models are a popular class of nonlinear dynamical models. Often polynomial basis expansion is used to describe the internal multivariate mapping (P-NARX). Resorting fixed functions convenient since it results in closed form solution estimation problem. The drawback, however, that predefined does not necessarily lead sparse representation...

great effect of three way catalytic convertor (twc) performance on oxygen sensor output voltage has made the sensor (located after catalyst) as the main signal in almost all today’s twc monitoring algorithms. in this paper output voltage of nonlinear oxygen sensor is estimated using a nonlinear autoregressive with exogenous inputs (narx) model. the estimation uses ecu calculated exhaust gas flo...

The PAM robot arm is belonged to highly nonlinear systems where perfect knowledge of their parameters is unattainable by conventional modeling techniques because of the timevarying inertia, hysteresis and other joint friction model uncertainties. To guarantee a good tracking performance, robust-adaptive control approaches combining conventional methods with new learning techniques are required....