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    Principles of System Identification

    Principles of System Identification

    - Theory and Practice

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    Bog E-bog
    • Format
    • Bog, hardback
    • Engelsk

    Beskrivelse

    Master Techniques and Successfully Build Models Using a Single Resource

    Vital to all data-driven or measurement-based process operations, system identification is an interface that is based on observational science, and centers on developing mathematical models from observed data. Principles of System Identification: Theory and Practice is an introductory-level book that presents the basic foundations and underlying methods relevant to system identification. The overall scope of the book focuses on system identification with an emphasis on practice, and concentrates most specifically on discrete-time linear system identification.

    Useful for Both Theory and Practice

    The book presents the foundational pillars of identification, namely, the theory of discrete-time LTI systems, the basics of signal processing, the theory of random processes, and estimation theory. It explains the core theoretical concepts of building (linear) dynamic models from experimental data, as well as the experimental and practical aspects of identification. The author offers glimpses of modern developments in this area, and provides numerical and simulation-based examples, case studies, end-of-chapter problems, and other ample references to code for illustration and training.

    Comprising 26 chapters, and ideal for coursework and self-study, this extensive text:



    Provides the essential concepts of identificationLays down the foundations of mathematical descriptions of systems, random processes, and estimation in the context of identificationDiscusses the theory pertaining to non-parametric and parametric models for deterministic-plus-stochastic LTI systems in detailDemonstrates the concepts and methods of identification on different case-studiesPresents a gradual development of state-space identification and grey-box modelingOffers an overview of advanced topics of identification namely the linear time-varying (LTV), non-linear, and closed-loop identificationDiscusses a multivariable approach to identification using the iterative principal component analysisEmbeds MATLAB® codes for illustrated examples in the text at the respective points



    Principles of System Identification: Theory and Practice

    presents a formal base in LTI deterministic and stochastic systems modeling and estimation theory; it is a one-stop reference for introductory to moderately advanced courses on system identification, as well as introductory courses on stochastic signal processing or time-series analysis.The MATLAB scripts and SIMULINK models used as examples and case studies in the book are also available on the author's website: http://arunkt.wix.com/homepage#!textbook/c397

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    Detaljer
    • SprogEngelsk
    • Sidetal910
    • Udgivelsesdato19-12-2014
    • ISBN139781439895993
    • Forlag CRC Press Inc
    • FormatHardback
    Størrelse og vægt
  • Vægt1782 g
    • coffee cup img
    10 cm
    book img
    17,8 cm
    25,4 cm

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    Sampling MATLAB MIMO systems Time-series analysis Time-delay estimation Discretization Conditional expectation Linear Time-Varying Systems Random processes Least-squares Estimators Kalman Filter Probability theory Signal-to-noise ratio Mean square error Spectral density Discrete Fourier transform (DFT) Fisher-Information State space model Principal Component Analysis (PCA) Random signals Estimation Methods Model development Power Spectral Density Deterministic Processes 2pi F0k Accuracy of Parametrized FRF Estimates Using PEM ACF Plot Ahead Prediction Algorithms for Estimating Specific Parametric Models ARIMA Model of Industrial Dryer Temperature AR Model ARX Model Auto-Regressive Integrated Moving Average Models Auto-Covariance Function Auto-regressive Models Auto-regressive Model Asymptotic Bias Bayesian Estimators Auto-Regressive Moving Average Models Cross-Spectral Density and Coherence Correlation Methods Discrete-Time LTI Systems Estimates of Correlation Estimation of ARIMA Models Estimation of Frequency Response Function Estimation of Correlation Functions Estimation of MA Models Closed-Loop Identification Fir Model Estimation of AR Models Estimation of Cross-Spectral Density Goodness of Estimators Convolution Equation Equation Error Model Estimation of Auto-Power Spectra Estimation of Signal Properties Cramer-Rao’s Inequality Identification of Parametric Time-Series Models Empirical Transfer Function (ETF) Estimation of ARMA Models Estimation of Mean and Variance Introduction to Modelling and Identification Estimating the Disturbance Spectrum Estimation of Coherency Fourier Transforms and Spectral Analysis FRF Frequency Response Function Linear Stationary Processes model structure Non-parametric and Parametric Models: Classification Models for Random Processes Models for Discrete-Time LTI Systems One-Step Ahead Prediction Multi-Step and Infinite-Step Ahead Predictions Partial coherence Noise Model Non-linear least squares Non-Parametric Descriptions parametric descriptions Power Spectral Density of a Random Process Impulse Response Estimation Prediction-Error Minimization (PEM) Methods Predictor Model: An Alternative LTI Description Random Signals and Processes Properties of the PEM Estimator SISO Identification Liquid Level System Transfer Function Form Time-Domain Analysis: Correlation Functions Spectral Characteristics of Standard Processes Spectral Density Estimation SS Model Linear Predictors Moving Average Models Sub-Space Identification non-linear identification Non-Parametric and Parametric Models Output Error Model Partial Correlation Functions Random Variables and Probability Partial CCF Transform-Domain Models for LTI Systems Variance and Distribution of PEM-QC Estimators Spectral Factorization Step Response Estimation Subspace Identification Algorithms Subspace Identification Method Transfer Function Operator

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