- ホーム
- > 洋書
- > 英文書
- > Science / Mathematics
Full Description
Identification of Continuous-time Models from Sampled Data presents an up-to-date view of this active area of research, describing recent methods and software tools and offering new results in areas such as: time and frequency domain optimal statistical approaches to identification; parametric identification for linear, nonlinear and stochastic systems; identification using instrumental variable, subspace and data compression methods; closed-loop and robust identification; and continuous-time modeling from non-uniformly sampled data and for systems with delay.
The CONTSID toolbox discussed in the final chapter gives an overview of developments and practical examples in which MATLAB® can be used for direct time-domain identification of continuous-time systems. A valuable reference for a broad audience drawn from researchers and graduate students in signal processing as well as in systems and control this book also covers material suitable for specialised graduate courses in these areas.
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-time 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.
