Continuous-time model identification from sampled data: Implementation issues and performance evaluation

Autor:	Michel Mensler, Alain Richard, Hugues Garnier
Rok vydání:	2003
Předmět:	Noise power Identification (information) Control and Systems Engineering Computer science SIGNAL (programming language) Monte Carlo method System identification Sensitivity (control systems) Transfer function Algorithm Toolbox Simulation Computer Science Applications
Zdroj:	International Journal of Control. 76:1337-1357
ISSN:	1366-5820 0020-7179
Popis:	This paper deals with equation error methods that fit continuous-time transfer function models to discrete-time data recently included in the CONTSID (CONtinuous-Time System IDentification) Matlab toolbox. An overview of the methods is first given where implementation issues are highlighted. The performances of the methods are then evaluated on simulated examples by Monte Carlo simulations. The experiments have been carried out to study the sensitivity of each approach to the design parameters, sampling period, signal-to-noise ratio, noise power spectral density and type of input signal. The effectiveness of the CONTSID toolbox techniques is also briefly compared with indirect methods in which discrete-time models are first estimated and then transformed into continuous-time models. The paper does not consider iterative or recursive algorithms for continuous-time transfer function model identification.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::5f81394df2fd325e3a86b667704c1fd2 https://doi.org/10.1080/0020717031000149636 Zobrazit plný text záznamu