This paper investigates the technique of the modeling and identification a new dynamic NARX fuzzy model by means of genetic algorithms. In conventional identification techniques, difficulties such as poor knowledge of the process, inaccurate process or complexity of the resulting mathematical model, all which limit their usefulness during dealing with dynamic nonlinear industrial processes. To ...

This section presents, an important class of multivariate non-linear models for discrete time: NARX (Nonlinear AutoRegressive with eXogenous inputs) feedforward NN [2]. The use of NARX Networks for forecasting has gained popularity due to its capacity to represent nonlinear systems with good precision and due to the fact that any data which can, in some way, supply information to the series of ...

The complex interactions between vapour-liquid equilibrium, mass and energy transports and reaction kinetics in reactive distillation (RD) process have constituted serious computational challenges to its modelling and control. This is because large numbers of nonlinear differential algebraic equations are needed to represent the process when using first principle modelling approach. This work d...

Model identification of polynomial NARX models involves a lengthy and computationally intensive procedure for selecting the model structure among a possibly large set of candidate regressors. If the model structure is under-parameterized to reduce the burden of the model selection phase, unsatisfactory results are generally obtained. This inaccuracy problem can be somewhat circumvented by focus...

The paper presents the application of support vector regression (SVR) to accurate forecasting of the tangential displacement of a concrete dam. The SVR nonlinear autoregressive model with exogenous inputs (NARX) was developed and tested using experimental data collected during fourteen years. A total of 573 data were used for training of the SVR model whereas the remaining 156 data were used to...

Due to the advances of network technologies and multimedia communications, Quality of Service (QoS) becomes an increasingly important issue in network communications. Many traditional assessment techniques were designed to evaluate the QoS of multimedia applications transmitted over these networks. In this paper, a new QoS evaluation system has been developed. The proposed system is based on us...

we have that Bayesian regression, a nonparametric identification technique with several appealing features, can be applied to the identification of NARX (nonlinear ARX) models. However, its computational complexity scales as O(N 3) where N is the data set size. In order to reduce complexity, the challenge is to obtain fixed-order parametric models capable of approximating accurately the nonpara...

This paper provides a formulation for using the delta-operator in the modelling of non-linear systems. It is shown that a unique representation of a deterministic non-linear auto-regressive with exogenous input (NARX) model can be obtained for polynomial basis functions using the delta-operator and expressions are derived to convert between the shiftand deltadomain. A delta-NARX model is applie...

Nitrate and nitrite control of anaerobic respiratory gene expression is mediated by dual two-component regulatory systems. The sensors NarX and NarQ each communicate nitrate and nitrite availability to the response regulators NarL and NarP. In the presence of nitrate, the NarX protein acts as a positive regulator ("kinase") of both NarL and NarP activity. In the presence of nitrite, the NarX pr...

As renewable energy increasingly integrates into the electric power system, electric load forecasting and renewable energy power generation forecasting become more important. In this project, ARIMA and NARX are applied to build load forecasting model focusing on improving statistical and computational efficiency without losing accuracy. ARIMA turns out to be better for short term forecasting wh...