Principles of system identification : theory and practice /: theory and practice. (2015)

• Record Type: Book
• Further Information: Note: Arun K. Tangirala.
• Authors: Tangirala, Arun K
• Contents: PART I INTRODUCTION TO IDENTIFICATION AND MODELS FOR LINEAR DETERMINISTIC SYSTEMS Introduction Motivation Historical developments System Identification Systematic identification Flow of learning material Software A Journey into Identification Identifiability Signal-to-Noise ratio Overfitting A modeling example: liquid level system Reflections and summary Mathematical Descriptions of Processes: Models Definition of a model Classification of models Models for Discrete-Time LTI Systems Convolution model Response models Difference equation form State-space descriptions Illustrative example in MATLAB: estimating LTI models Summary Transform-Domain Models for Linear Time-Invariant Systems Frequency response function Transfer function form Empirical transfer function (ETF) Closure Sampling and Discretization Discretization Sampling Summary PART II MODELS FOR RANDOM PROCESSES Random Processes Introductory remarks Random variables and probability Probability theory Statistical properties of random variables Random signals and processes Time-series analysis Summary Time-Domain Analysis: Correlation Functions Motivation Auto-covariance function White-noise process Cross-covariance function Partial correlation functions Summary Models for Linear Stationary Processes Motivation Basic ideas Linear stationary processes Moving average models Auto-regressive models Auto-regressive moving average models Auto-regressive integrated moving average models Summary Fourier Analysis and Spectral Analysis of Deterministic Signals Motivation Definitions Fourier representations of deterministic processes Discrete Fourier Transform (DFT) Summary Spectral Representations of Random Processes Introduction Power spectral density of a random process Spectral characteristics of standard processes Cross-spectral density and coherence Partial coherence Spectral factorization Summary PART III ESTIMATION METHODS Introduction to Estimation Motivation A simple example: constant embedded in noise Definitions and terminology Types of estimation problems Estimation methods Historical notes Goodness of Estimators Introduction Fisher information Bias Variance Efficiency Sufficiency Cramer-Rao’s inequality Asymptotic bias Mean square error Consistency Distribution of estimates Hypothesis testing and confidence intervals Empirical methods for hypothesis testing Summary Appendix Estimation Methods: Part I Introduction Method of moments estimators Least squares estimators Non-linear least squares Summary Appendix Estimation Methods: Part II Maximum likelihood estimators Bayesian estimators Summary Estimation of Signal Properties Introduction Estimation of mean and variance Estimators of correlation Estimation of correlation functions Estimation of auto-power Spectra Estimation of cross-spectral density Estimation of coherence Summary PART IV IDENTIFICATION OF DYNAMIC MODELS - CONCEPTS AND PRINCIPLES Non-Parametric and Parametric Models for Identification Introduction The overall model Quasi-stationarity Non-parametric descriptions Parametric descriptions Summary Predictions Introduction Conditional expectation and linear predictors One-step ahead prediction and innovations Multi-step and infinite-step ahead predictions Predictor model: An alternative LTI description Identifiability Summary Identification of Parametric Time-Series Models Introduction Estimation of AR models Estimation of MA models Estimation of ARMA models Summary Identification of Non-Parametric Input-Output Models Recap Impulse response estimation Step response estimation Estimation of frequency response function Estimating the disturbance spectrum Summary Identification of Parametric Input-Output Models Recap Prediction-error minimization (PEM) methods Properties of the PEM estimator Variance and distribution of PEM-QC estimators Accuracy of parametrized FRF estimates using PEM Algorithms for estimating specific parametric models Correlation methods Summary Statistical and Practical Elements of Model Building Introduction Informative Data Input design for identification Data pre-processing Time-delay estimation Model development Summary Identification of State-Space Models Introduction Mathematical essentials and basic ideas Kalman filter Foundations for subspace identification Preliminaries for subspace identification methods Subspace identification algorithms Structured state-space models Summary Case Studies ARIMA model of industrial dryer temperature Simulated process: developing an input-output model Process with random walk noise Multivariable modeling of a four-tank system Summary PART V ADVANCED CONCEPTS Advanced Topics in SISO Identification Identification of linear time-varying systems Non-linear identification Closed-loop identification Summary Linear Multivariable Identification Motivation Estimation of time delays in MIMO systems Principal component analysis (PCA) Summary References Index
• Edition: 1st
• Publisher Details: Boca Raton : CRC Press
• Publication Date: 2015
• Extent: 1 online resource, illustrations
• Subjects: 003.1; System identification
• Languages: English
• ISBNs: 9781439896020
• Related ISBNs: 9781498723688
• Notes: Note: Description based on CIP data; resource not viewed.
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