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Matlab software for recursive identification and scaling using a structured nonlinear black—box model – revision 2”, technical reports from the department of information technology 2005-022 (2005).
| Content Provider | CiteSeerX |
|---|---|
| Author | Brus, Torbjörn Wigren Linda |
| Abstract | This report is intended as a users manual for a package of MATLAB ™ scripts and functions, developed for recursive prediction error identification of nonlinear state space systems and nonlinear static systems. The core of the package is an implementation of an output error identification and scaling algorithm. The algorithm is based on a continuous time, structured black box state space model of a nonlinear system. Furthermore, to initialize the algorithm an algorithm based on Kalman filter theory is included. The purpose of the initialization algorithm is to find initial parameters for the prediction error algorithm, and thus reducing the risk of convergence to local false minima. An RPEM algorithm for recursive identification of nonlinear static systems, that re-uses the parameterization of the nonlinear ODE model, is also included in the software package. In this version of the software the discretization of the continuous time model is based on the midpoint integration algorithm. The software can only be run off-line, i.e. no true real time operation is possible. The algorithms are however implemented so that true on-line operation can be obtained by extraction of the main algorithmic loop. The user must then provide the real time environment. The software package contains scripts and functions that allow the user to either input live measurements or to generate test |
| File Format | |
| Publisher Date | 2005-01-01 |
| Access Restriction | Open |
| Subject Keyword | Recursive Identification Technical Report Information Technology Matlab Software Nonlinear Static System Software Package Midpoint Integration Algorithm Matlab Script Nonlinear State Space System Output Error Identification Rpem Algorithm Continuous Time Model Continuous Time Initial Parameter Local False Minimum Main Algorithmic Loop True Real Time Operation Nonlinear System Recursive Prediction Error Identification Prediction Error Algorithm Kalman Filter Theory Nonlinear Ode Model Input Live Measurement Real Time Environment Initialization Algorithm True On-line Operation |
| Content Type | Text |
