141 To generate identification data, we simulated this system using the feedback law u(t) = r(t) 0 (00:95q 02)y(t) = r(t) + 0:95y(t 0 2) (43) which places the closed-loop poles in 0.8618 and 0.6382. In the simulation we used independent, zero-mean, Gaussian white noise reference and noise signals fr(t)g and fe(t)g with variances 1 and 0.01, respectively. N = 200 data samples were used. In Table...

The control of car following is essential due to its safety and its operational efficiency. For this purpose, this paper builds a model of car following behavior based on ARMAX structure from a real traffic dataset and design a Model Predictive Control (MPC) system. Based on the relative distance and relative acceleration of each instant, the MPC predicts the future behavior of the leader vehic...

Large-range dependence (LRD) is essential phenomena both in LAN and WAN data traffic. Modeling such traffic traces is significant to understand the nature of the original traffic and to synthesize simulation traffic traces. Existing work such as multi-fractal wavelet model (MWM) was proposed to model IP traces and has reached much exactness. However, the nature of IP traffic exploits burst that...

This paper addresses reconstruction of linear dynamic networks from heterogeneous datasets. Those datasets consist of measurements from linear dynamical systems in multiple experiment subjected to different experimental conditions, e.g., changes/perturbations in parameters, disturbance or noise. A main assumption is that the Boolean structures of the underlying networks are the same in all expe...

A general approach for system identification with artificial neural networks is presented. A dynamic process can be represented as a function of the evolution of the inputs/outputs of the system. The standard approach to system identification is to describe the system by a set of equations that take into account the underlying mechanisms of the studied plant. If the equations of the system are ...

Modelling of heat exchanger helps to define the error that occurs during the operation. Hence by optimizing it using genetic algorithm and particle swarm optimization, the error that occurred could be minimized and compared between both algorithms.The primary objective of this study was to obtain structural model using Autoregressive Moving Average Exogenous (ARMAX) equation. In this study, dat...

Economic and financial processes are complex and highly nonlinear. However, somewhat surprisingly, linear models like ARMAXGARCH often describe these processes reasonably well. In this paper, we provide a possible explanation for the empirical success of these models. 1 Formulation of the Problem Economic and financial processes are very complex. It is well know that economic and financial proc...

With the ever-increasing growth of energy demand and costs, process monitoring operational costs is great importance for industries. In this light, both financial budget management local optimization supposed to be guaranteed properly. To achieve goal, a support vector machine recursive feature elimination (SVM-RFE) method together with clustering algorithm was developed extract features while ...

The main goal of this paper consists in expressing the solution of a Stein equation in terms of the Fisher information matrix (FIM) of a scalar ARMAX process. A condition for expressing the FIM in terms of a solution to a Stein equation is also set forth. Such interconnections can be derived when a companion matrix with eigenvalues equal to the roots of an appropriate polynomial associated with...