Titlebook: Identification of Continuous-time Models from Sampled Data; Hugues Garnier,Liuping Wang Book 2008 Springer-Verlag London 2008 Analysis.MAT

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Book 2008 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-loo

永久

Continuous-time Model Identification Using Spectrum Analysis with Passivity-preserving Model Reductature on continuous-time model identification involving approaches with pseudo-linear regression, correlation and gradient search methods, there are algorithmic contributions [7, 15, 32, 41, 43, 44]; with surveys of Young [44], Unbehauen and Rao [13, 34, 35]; stochastic model estimation aspects [17]; software and algorithmic aspects [11, 40].

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Estimation of Continuous-time Stochastic System Parameters,igue crack growth in materials can be found in [50], and models of human head movements appear in [20]. Identification of ship-steering dynamics by means of linear CT models and the maximum likelihood (ML) method is considered in [6]. Numerous other examples of applications of stochastic differentia

上腭

Subspace-based Continuous-time Identification,of continuous-time models and such models require suitable methods of system identification [19]. To this end, a theoretical framework of continuous-time identification and statistical model validation is needed. In particular, as experimental data are usually provided as time series, it is relevant

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Identification of Continuous-time Models from Sampled Data

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Book 2008continuous-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..