Identification of continuous-time models from sampled data
Author(s)
Bibliographic Information
Identification of continuous-time models from sampled data
（Advances in industrial control）
Springer, c2008
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Note
Includes bibliographical references and index
Description and Table of Contents
Description
This is the first book dedicated to direct continuous-time model identification for 15 years. It cuts down on time spent hunting through journals by providing an overview of much recent research in an increasingly busy field. The CONTSID toolbox discussed in the final chapter gives an overview of developments and practical examples in which MATLAB (R) can be used for direct time-domain identification of continuous-time systems. This is a valuable reference for a broad audience.
Table of Contents
Direct Identification of Continuous-time Models from Sampled Data: Issues, Basic Solutions and Relevance.- Estimation of Continuous-time Stochastic System Parameters.- Robust Identification of Continuous-time Systems from Sampled Data.- Refined Instrumental Variable Identification of Continuous-time Hybrid Box-Jenkins Models.- Instrumental Variable Methods for Closed-loop Continuous Model Identification.- Model Order Identification for Continuous-time Models.- Estimation of the Parameters of Continuous-time Systems Using Data Compression.- Frequency-domain Approach to Continuous-time System Identification: Some Practical Aspects.- The CONTSID Toolbox: A Software Support for Data-based Continuous-time Modelling.- Subspace-based Continuous-time Identification.- Process Parameter and Delay Estimation from Non-uniformly Sampled Data.- Iterative Methods for Identification of Multiple Input Continuous-time Systems with Unknown Time Delays.- Closed-loop Parametric Identification for Continuous-time Linear Systems via New Algebraic Techniques.- Continuous-time Model Identification Using Spectrum Analysis with Passivity-preserving Model Reduction.
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