This book explains the theoretical concepts of building (linear) dynamic models from experimental data, their practical implementations, and experimental aspects with data pre-treatment. Starting with a quick tour on identification, the first part provides foundations of discrete-time deterministic LTI systems. The second part describes modeling of stochastic stationary processes. The third part explains estimation theory and methods in detail with illustrative examples. The core material in parts four and five presents model descriptions, classical and modern identification methods, practical…mehr
This book explains the theoretical concepts of building (linear) dynamic models from experimental data, their practical implementations, and experimental aspects with data pre-treatment. Starting with a quick tour on identification, the first part provides foundations of discrete-time deterministic LTI systems. The second part describes modeling of stochastic stationary processes. The third part explains estimation theory and methods in detail with illustrative examples. The core material in parts four and five presents model descriptions, classical and modern identification methods, practical and experimental aspects with case studies. 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.
Arun K. Tangirala an Associate Professor at the Department of Chemical Engineering, IIT Madras, India. He obtained his B. Tech. (Chemical Engineering) from IIT Madras, India and Ph.D. (Process Control & Monitoring) from the University of Alberta, Canada in the years 1996 and 2001, respectively. Dr. Tangirala specializes in process control, modelling, monitoring and multivariate data analysis. His research group is focused on solving some of the cutting edge problems in data-driven analysis and modelling. A recipient of different teaching and research awards, he has conducted several workshops and short-term courses on data analysis and process identification.
Inhaltsangabe
PART I INTRODUCTION TO IDENTIFICATION AND MODELS FOR LINEAR DETERMINISTIC SYSTEMS. Introduction. A Journey into Identification. Mathematical Descriptions of Processes: Models. Models for Discrete-Time LTI Systems. Transform-Domain Models for Linear TIme-Invariant Systems. Sampling and Discretization. PART II MODELS FOR RANDOM PROCESSES. Random Processes. Time-Domain Analysis: Correlation Functions. Models for Linear Stationary Processes. Fourier Analysis and Spectral Analysis of Deterministic Signals. Spectral Representations of Random Processes. PART III ESTIMATION METHODS. Introduction to Estimation. Goodness of Estimators. Estimation Methods: Part I. Estimation Methods: Part II. Estimation of Signal Properties. PART IV IDENTIFICATION OF DYNAMIC MODELS - CONCEPTS AND. PRINCIPLES. Non-Parametric and Parametric Models for Identification. Predictions. Identification of Parametric Time-Series Models. Identification of Non-Parametric Input-Output Models. Identification of Parametric Input-Output Models. Statistical and Practical Elements of Model Building. Identification of State-Space Models. Case Studies. PART V ADVANCED CONCEPTS. Advanced Topics in SISO Identification. Linear Multivariable Identification. References. Index.
PART I INTRODUCTION TO IDENTIFICATION AND MODELS FOR LINEAR DETERMINISTIC SYSTEMS. Introduction. A Journey into Identification. Mathematical Descriptions of Processes: Models. Models for Discrete-Time LTI Systems. Transform-Domain Models for Linear TIme-Invariant Systems. Sampling and Discretization. PART II MODELS FOR RANDOM PROCESSES. Random Processes. Time-Domain Analysis: Correlation Functions. Models for Linear Stationary Processes. Fourier Analysis and Spectral Analysis of Deterministic Signals. Spectral Representations of Random Processes. PART III ESTIMATION METHODS. Introduction to Estimation. Goodness of Estimators. Estimation Methods: Part I. Estimation Methods: Part II. Estimation of Signal Properties. PART IV IDENTIFICATION OF DYNAMIC MODELS - CONCEPTS AND. PRINCIPLES. Non-Parametric and Parametric Models for Identification. Predictions. Identification of Parametric Time-Series Models. Identification of Non-Parametric Input-Output Models. Identification of Parametric Input-Output Models. Statistical and Practical Elements of Model Building. Identification of State-Space Models. Case Studies. PART V ADVANCED CONCEPTS. Advanced Topics in SISO Identification. Linear Multivariable Identification. References. Index.
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