System Identification With Matlab Linear Models Identification

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System Identification with MATLAB. Linear Models

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Author : Marvin L.
Publisher : Createspace Independent Publishing Platform
ISBN 13 : 9781539691891
Total Pages : 456 pages
Book Rating : 4.6/5 (918 download)

Book Synopsis System Identification with MATLAB. Linear Models by : Marvin L.

Download or read book System Identification with MATLAB. Linear Models written by Marvin L. and published by Createspace Independent Publishing Platform. This book was released on 2016-10-23 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: In System Identification Toolbox software, MATLAB represents linear systems as model objects. Model objects are specialized data containers that encapsulate model data and other attributes in a structured way. Model objects allow you to manipulate linear systems as single entities rather than keeping track of multiple data vectors, matrices, or cell arrays. Model objects can represent single-input, single-output (SISO) systems or multiple-input, multiple-output (MIMO) systems. You can represent both continuous- and discrete-time linear systems. The toolbox provides several linear and nonlinear black-box model structures, which have traditionally been useful for representing dynamic systems. This book develops the next tasks with linear models:* "Black-Box Modeling" * "Identifying Frequency-Response Models" * "Identifying Impulse-Response Models" * "Identifying Process Models" * "Identifying Input-Output Polynomial Models" * "Identifying State-Space Models" * "Identifying Transfer Function Models" * "Refining Linear Parametric Models"* "Refine ARMAX Model with Initial Parameter Guesses at Command Line"* "Refine Initial ARMAX Model at Command Line" * "Extracting Numerical Model Data" * "Transforming Between Discrete-Time and Continuous-Time Representations" * "Continuous-Discrete Conversion Methods" * "Effect of Input Intersample Behavior on Continuous-Time Models" * "Transforming Between Linear Model Representations" * "Subreferencing Models"* "Concatenating Models" * "Merging Models"* "Building and Estimating Process Models Using System Identification Toolbox* "Determining Model Order and Delay" 5* "Model Structure Selection: Determining Model Order and Input Delay" * "Frequency Domain Identification: Estimating Models Using Frequency Domain Data" * "Building Structured and User-Defined Models Using System Identification Toolbox"

Principles of System Identification

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Author : Arun K. Tangirala
Publisher : CRC Press
ISBN 13 : 143989602X
Total Pages : 908 pages
Book Rating : 4.4/5 (398 download)

Book Synopsis Principles of System Identification by : Arun K. Tangirala

Download or read book Principles of System Identification written by Arun K. Tangirala and published by CRC Press. This book was released on 2018-10-08 with total page 908 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 identification Lays down the foundations of mathematical descriptions of systems, random processes, and estimation in the context of identification Discusses the theory pertaining to non-parametric and parametric models for deterministic-plus-stochastic LTI systems in detail Demonstrates the concepts and methods of identification on different case-studies Presents a gradual development of state-space identification and grey-box modeling Offers an overview of advanced topics of identification namely the linear time-varying (LTV), non-linear, and closed-loop identification Discusses a multivariable approach to identification using the iterative principal component analysis Embeds 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

System Identification with MATLAB. Non Linear Models, Odes and Time Series

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Author : Marvin L.
Publisher : Createspace Independent Publishing Platform
ISBN 13 : 9781539692317
Total Pages : 366 pages
Book Rating : 4.6/5 (923 download)

Book Synopsis System Identification with MATLAB. Non Linear Models, Odes and Time Series by : Marvin L.

Download or read book System Identification with MATLAB. Non Linear Models, Odes and Time Series written by Marvin L. and published by Createspace Independent Publishing Platform. This book was released on 2016-10-23 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt: In System Identification Toolbox software, MATLAB represents linear systems as model objects. Model objects are specialized data containers that encapsulate model data and other attributes in a structured way. Model objects allow you to manipulate linear systems as single entities rather than keeping track of multiple data vectors, matrices, or cell arrays. Model objects can represent single-input, single-output (SISO) systems or multiple-input, multiple-output (MIMO) systems. You can represent both continuous- and discrete-time linear systems. Thisb book develops de next task with models: Nonlinear Black-Box Model Identification Nonlinear Model Identification Fit Nonlinear Models Identifying Nonlinear ARX Models Nonlinearity Estimators for Nonlinear ARX Models Estimate Nonlinear ARX Models in the GUI Estimate Nonlinear ARX Models at the Command Line Validating Nonlinear ARX Models Identifying Hammerstein-Wiener Models Nonlinearity Estimators for Hammerstein-Wiener Models Estimation Algorithm for Hammerstein-Wiener Models Validating Hammerstein-Wiener Models Linear Approximation of Nonlinear Black-Box Models ODE Parameter Estimation (Grey-Box Modeling) Estimating Linear Grey-Box Models Estimating Nonlinear Grey-Box Models After Estimating Grey-Box Models Estimating Coefficients of ODEs to Fit Given Solution Estimate Model Using Zero/Pole/Gain Parameters Time Series Identification Estimating Time-Series Power Spectra Estimate Time-Series Power Spectra Using the GUI Estimate Time-Series Power Spectra at the Command Line Estimating AR and ARMA Models Estimating Polynomial Time-Series Models in the GUI Estimating AR and ARMA Models at the Command Line Estimating State-Space Time-Series Models Estimating State-Space Models at the Command Line Identify Time-Series Models at Command Line Estimating Nonlinear Models for Time-Series Data Estimating ARIMA Models Analyzing of Time-Series Models Recursive Model Identification General Form of Recursive Estimation Algorithm Kalman Filter Algorithm Recursive Estimation and Data Segmentation Techniques in System Identification Toolbox Model Analysis Validating Models After Estimation Plotting Models in the GUI Simulating and Predicting Model Output Simulation and Prediction in the GUI Simulation and Prediction at the Command Line Predict Using Time-Series Model Residual Analysis Impulse and Step Response Plots Frequency Response Plots Displaying the Confidence Interval Noise Spectrum Plots Pole and Zero Plots Analyzing MIMO Models Akaike's Criteria for Model Validation Troubleshooting Models Unstable Models Missing Input Variables Complicated Nonlinearities Spectrum Estimation Using Complex Data System Identification Toolbox Blocks Using System Identification Toolbox Blocks in Simulink Models Identifying Linear Models Simulating Identified Model Output in Simulink Simulate Identified Model Using Simulink Software System Identification Tool GUI

System Identification With Matlab. Linear Models Identification

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Author : A. Smith
Publisher : Createspace Independent Publishing Platform
ISBN 13 : 9781979771122
Total Pages : 320 pages
Book Rating : 4.7/5 (711 download)

Book Synopsis System Identification With Matlab. Linear Models Identification by : A. Smith

Download or read book System Identification With Matlab. Linear Models Identification written by A. Smith and published by Createspace Independent Publishing Platform. This book was released on 2017-11-16 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book develops the work with Linear Models Identification, State Space Models, Transfer Function Models, Impulse Response Models and Frequency Response Models. Linear Models Identification includes ARMAX Models and other General Polynomial Models. The most important content that this book provides are the following: - Linear Model Identification - Refine Linear Parametric Models - Refine ARMAX Model with Initial Parameter Guesses at Command Line - Refine Initial ARMAX Model at Command Line - Extracting Numerical Model - DataTransforming Between Discrete-Time and Continuous-Time Representations - Continuous-Discrete Conversion Methods - Effect of Input Intersample Behavior on Continuous-Time Models - Transforming Between Linear Model Representations - Treating Noise Channels as Measured Inputs - Concatenating Models - Merging Models - Determining Model Order and Delay - Model Structure Selection: Determining Model Order and Input Delay - Frequency Domain Identification: Estimating Models Using Frequency Domain Data - Building Structured and User-Defined Models Using System Identification Toolbox - Identifying Process Models - Estimate Process Models Using the App - Estimate Process Models at the Command Line - Building and Estimating Process Models Using System - Identification Toolbox - Process Model Structure Specification - Estimating Multiple-Input, Multi-Output Process Models - Identifying Input-Output Polynomial Models - Estimate Polynomial Models in the App - Estimate Polynomial Models at the Command Line - Polynomial Model Estimation Algorithms - Estimate Models Using ARMAX - Identifying State-Space Models - Estimate State-Space Model With Order Selection - Estimate State-Space Models in System Identification App - Estimate State-Space Models at the Command Line - Estimate State-Space Models with Free-Parameterization - Estimate State-Space Models with Canonical Parameterization - Estimate State-Space Equivalent of ARMAX and OE Models - State-Space Model Estimation Methods - Identifying Transfer Function Models - Estimate Transfer Function Models in the System Identification App - Estimate Transfer Function Models at the Command Line - Estimate Transfer Functions with Delays - Identifying Frequency-Response Models - Estimate Frequency-Response Models in the App - Estimate Frequency-Response Models at the Command Line - Selecting the Method for Computing Spectral Models - Identifying Impulse-Response Models - Estimate Impulse-Response Models Using System Identification App - Estimate Impulse-Response Models at the Command Line

System Identification

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Author : Lennart Ljung
Publisher : Pearson Education
ISBN 13 : 0132440539
Total Pages : 875 pages
Book Rating : 4.1/5 (324 download)

Book Synopsis System Identification by : Lennart Ljung

Download or read book System Identification written by Lennart Ljung and published by Pearson Education. This book was released on 1998-12-29 with total page 875 pages. Available in PDF, EPUB and Kindle. Book excerpt: The field's leading text, now completely updated. Modeling dynamical systems — theory, methodology, and applications. Lennart Ljung's System Identification: Theory for the User is a complete, coherent description of the theory, methodology, and practice of System Identification. This completely revised Second Edition introduces subspace methods, methods that utilize frequency domain data, and general non-linear black box methods, including neural networks and neuro-fuzzy modeling. The book contains many new computer-based examples designed for Ljung's market-leading software, System Identification Toolbox for MATLAB. Ljung combines careful mathematics, a practical understanding of real-world applications, and extensive exercises. He introduces both black-box and tailor-made models of linear as well as non-linear systems, and he describes principles, properties, and algorithms for a variety of identification techniques: Nonparametric time-domain and frequency-domain methods. Parameter estimation methods in a general prediction error setting. Frequency domain data and frequency domain interpretations. Asymptotic analysis of parameter estimates. Linear regressions, iterative search methods, and other ways to compute estimates. Recursive (adaptive) estimation techniques. Ljung also presents detailed coverage of the key issues that can make or break system identification projects, such as defining objectives, designing experiments, controlling the bias distribution of transfer-function estimates, and carefully validating the resulting models. The first edition of System Identification has been the field's most widely cited reference for over a decade. This new edition will be the new text of choice for anyone concerned with system identification theory and practice.

System Identification With Matlab. Create Linear and Nonlinear Dynamic System Models

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Author : A. Taylor
Publisher : Createspace Independent Publishing Platform
ISBN 13 : 9781979722759
Total Pages : 226 pages
Book Rating : 4.7/5 (227 download)

Book Synopsis System Identification With Matlab. Create Linear and Nonlinear Dynamic System Models by : A. Taylor

Download or read book System Identification With Matlab. Create Linear and Nonlinear Dynamic System Models written by A. Taylor and published by Createspace Independent Publishing Platform. This book was released on 2017-11-14 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: System Identification Toolbox provides MATLAB functions, Simulink blocks, and an app for constructing mathematical models of dynamic systems from measured input-output data. It lets you create and use models of dynamic systems not easily modeled from first principles or specifications. You can use time-domain and frequency-domain input-output data to identify continuous-time and discrete-time transfer functions, process models, and state-space models. The toolbox also provides algorithms for embedded online parameter estimation. The toolbox provides identification techniques such as maximum likelihood, prediction-error minimization (PEM), and subspace system identification. To represent nonlinear system dynamics, you can estimate Hammerstein-Weiner models and nonlinear ARX models with wavelet network, tree-partition, and sigmoid network nonlinearities. The toolbox performs grey-box system identification for estimating parameters of a user-defined model. You can use the identified model for system response prediction and plant modeling in Simulink. The toolbox also supports time-series data modeling and time-series forecasting. The most important content that this book provides are the following: - System Identification Overview - What Is System Identification? - About Dynamic Systems and Models - System Identification Requires Measured Data - Building Models from Data - Black-Box Modeling - Grey-Box Modeling - Evaluating Model Quality - When to Use the App vs. the Command Line - System Identification Workflow - Commands for Model Estimation - Linear Model Identification - Identify Linear Models Using System Identification App - Preparing Data for System Identification - Saving the Session - Estimating Linear Models Using Quick Start - Estimating Linear Models - Viewing Model Parameters - Exporting the Model to the MATLAB Workspace - Exporting the Model to the Linear System Analyzer - Identify Linear Models Using the Command Line - Preparing Data - Estimating Impulse Response Models - Estimating Delays in the Multiple-Input System - Estimating Model Orders Using an ARX Model Structure - Estimating Transfer Functions - Estimating Process Models - Estimating Black-Box Polynomial Models - Simulating and Predicting Model Output - Identify Low-Order Transfer Functions (Process Models) - Using System Identification App - What Is a Continuous-Time Process Model? - Preparing Data for System Identification - Estimating a Second-Order Transfer Function (Process Model) - with Complex Poles - Estimating a Process Model with a Noise Component - Viewing Model Parameters - Exporting the Model to the MATLAB Workspace - Simulating a System Identification Toolbox Model in Simulink Software - Estimating Models Using Frequency-Domain Data - Advantages of Using Frequency-Domain Data - Representing Frequency-Domain Data in the Toolbox - Preprocessing Frequency-Domain Data for Model - Estimation - Estimating Linear Parametric Models - Validating Estimated Model - Next Steps After Identifying a Model - Nonlinear Model Identification - Identify Nonlinear Black-Box Models Using System - Identification App - What Are Nonlinear Black-Box Models? - Preparing Data - Estimating Nonlinear ARX Models - Estimating Hammerstein-Wiener Models

Mastering System Identification in 100 Exercises

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Author : Johan Schoukens
Publisher : John Wiley & Sons
ISBN 13 : 1118218507
Total Pages : 285 pages
Book Rating : 4.1/5 (182 download)

Book Synopsis Mastering System Identification in 100 Exercises by : Johan Schoukens

Download or read book Mastering System Identification in 100 Exercises written by Johan Schoukens and published by John Wiley & Sons. This book was released on 2012-04-02 with total page 285 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book enables readers to understand system identification and linear system modeling through 100 practical exercises without requiring complex theoretical knowledge. The contents encompass state-of-the-art system identification methods, with both time and frequency domain system identification methods covered, including the pros and cons of each. Each chapter features MATLAB exercises, discussions of the exercises, accompanying MATLAB downloads, and larger projects that serve as potential assignments in this learn-by-doing resource.

System Identification With Matlab

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Author : A. Taylor
Publisher :
ISBN 13 : 9781979733977
Total Pages : 330 pages
Book Rating : 4.7/5 (339 download)

Book Synopsis System Identification With Matlab by : A. Taylor

Download or read book System Identification With Matlab written by A. Taylor and published by . This book was released on 2017-11-14 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: System Identification Toolbox provides MATLAB functions, Simulink blocks, and an app for constructing mathematical models of dynamic systems from measured input-output data. It lets you create and use models of dynamic systems not easily modeled from first principles or specifications. You can use time-domain and frequency-domain input-output data to identify continuous-time and discrete-time transfer functions, process models, and state-space models. The toolbox also provides algorithms for embedded online parameter estimation.The most important content that this book provides are the following:Choosing Your System Identification ApproachWhat Are Model Objects?Model Objects Represent Linear SystemsAbout Model DataTypes of Model ObjectsDynamic System ModelsNumeric ModelsNumeric Linear Time Invariant (LTI) ModelsIdentified LTI ModelsIdentified Nonlinear ModelsAbout Identified Linear ModelsWhat are IDLTI Models?Measured and Noise Component ParameterizationsLinear Model EstimationLinear Model StructuresAbout System Identification Toolbox Model ObjectsWhen to Construct a Model Structure Independently of EstimationCommands for Constructing Linear Model StructuresModel PropertiesAvailable Linear ModelsEstimation ReportCompare Estimated Models Using Estimation ReportAnalyze and Refine Estimation Results Using Estimation ReportImposing Constraints on Model Parameter ValuesRecommended Model Estimation SequenceSupported Models for Time- and Frequency-Domain DataSupported Models for Time-Domain DataSupported Models for Frequency-Domain DataSupported Continuous- and Discrete-Time ModelsModel Estimation CommandsModeling Multiple-Output SystemsAbout Modeling Multiple-Output SystemsModeling Multiple Outputs DirectlyModeling Multiple Outputs as a Combination of Single-Output ModelsImproving Multiple-Output Estimation Results by WeighingOutputs During EstimationRegularized Estimates of Model ParametersWhat Is Regularization?When to Use RegularizationChoosing Regularization ConstantsEstimate Regularized ARX Model Using System Identification AppLoss Function and Model Quality MetricsWhat is a Loss Function?Options to Configure the Loss FunctionModel Quality MetricsRegularized Identification of Dynamic SystemsData Import and ProcessingSupported DataWays to Obtain Identification DataWays to Prepare Data for System IdentificationRequirements on Data SamplingRepresenting Data in MATLAB WorkspaceTime-Domain Data RepresentationTime-Series Data RepresentationFrequency-Domain Data RepresentationImport Time-Domain Data into the AppImport Frequency-Domain Data into the AppTransform DataIdentifying Process ModelsWhat Is a Process Model?Data Supported by Process ModelsEstimate Process Models Using the App and Command LineBuilding and Estimating Process Models Using System Identification ToolboxProcess Model Structure SpecificationEstimating Multiple-Input, Multi-Output Process Models" Disturbance Model Structure for Process ModelsSpecifying Initial Conditions for Iterative Estimation Algorithms

System Identification With Matlab

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Author : A. Smith
Publisher : Createspace Independent Publishing Platform
ISBN 13 : 9781979799911
Total Pages : 264 pages
Book Rating : 4.7/5 (999 download)

Book Synopsis System Identification With Matlab by : A. Smith

Download or read book System Identification With Matlab written by A. Smith and published by Createspace Independent Publishing Platform. This book was released on 2017-11-19 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book develops the work with Nonlinear Models and Time Series Identification. To represent nonlinear system dynamics, you can estimate Hammerstein-Weiner models and nonlinear ARX models with wavelet network, tree-partition, and sigmoid network nonlinearities. MATLAB System Identification Toolbox performs grey-box system identification for estimating parameters of a user-defined model. You can use the identified model for system response prediction and plant modeling in Simulink. The toolbox also supports time-series data modeling and time-series forecasting.. It is possible to analyze time series data by identifying linear and nonlinear models, including AR, ARMA, and state-space models; forecast values The most important content that this book provides are the following: - When to Fit Nonlinear Models - Nonlinear Model Estimation - Nonlinear Model Structures - Nonlinear ARX Models - Hammerstein-Wiener Models - Nonlinear Grey-Box Models - Preparing Data for Nonlinear Identification - Identifying Nonlinear ARX Models - Prepare Data for Identification - Configure Nonlinear ARX Model Structure - Specify Estimation Options for Nonlinear ARX Models - Initialize Nonlinear ARX Estimation Using Linear Model - Estimate Nonlinear ARX Models in the App - Estimate Nonlinear ARX Models at the Command Line - Estimate Nonlinear ARX Models Initialized Using Linear ARX Models - Validate Nonlinear ARX Models - Using Nonlinear ARX Models - Linear Approximation of Nonlinear Black-Box Models - Nonlinear Black-Box Model Identification - Identifying Hammerstein-Wiener Models - Available Nonlinearity Estimators for Hammerstein-Wiener Models - Estimate Hammerstein-Wiener Models in the App . - Estimate Hammerstein-Wiener Models at the Command Line - Validating Hammerstein-Wiener Models - How the Software Computes Hammerstein-Wiener Model Output - Evaluating Nonlinearities (SISO) - Evaluating Nonlinearities (MIMO) - Simulation of Hammerstein-Wiener Model - Estimate Hammerstein-Wiener Models Initialized Using Linear OE Models - Estimate Linear Grey-Box Models - Estimate Continuous-Time Grey-Box Model for Heat Diffusion - Estimate Discrete-Time Grey-Box Model with Parameterized Disturbance - Estimate Coefficients of ODEs to Fit Given Solution - Estimate Model Using Zero/Pole/Gain Parameters - Estimate Nonlinear Grey-Box Models - Identifying State-Space Models with Separate Process and Measurement Noise Descriptions - Time Series Identification - Preparing Time-Series Data - Estimate Time-Series Power Spectra - Estimate AR and ARMA Models - Definition of AR and ARMA Models - Estimating Polynomial Time-Series Models in the App - Estimating AR and ARMA Models at the Command Line - Estimate State-Space Time Series Models - Identify Time-Series Models at the Command Line - Estimate ARIMA Models - Analyze Time-Series Models - Introduction to Forecasting of Dynamic System Response - Forecasting Time Series Using Linear Models - Forecasting Response of Linear Models with Exogenous Inputs - Forecasting Response of Nonlinear Models - Forecast the Output of a Dynamic System - Forecast Time Series Data Using an ARMA Model - Recursive Model Identification

Basic System Identification with MATLAB

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Author : Kendall T.
Publisher : Createspace Independent Publishing Platform
ISBN 13 : 9781539772279
Total Pages : 222 pages
Book Rating : 4.7/5 (722 download)

Book Synopsis Basic System Identification with MATLAB by : Kendall T.

Download or read book Basic System Identification with MATLAB written by Kendall T. and published by Createspace Independent Publishing Platform. This book was released on 2016-10-27 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: System Identification Toolbox constructs mathematical models of dynamic systems from measured input-output data. It provides MATLAB(r) functions, Simulink blocks, and an interactive tool for creating and using models of dynamic systems not easily modeled from first principles or specifications You can use time-domain and frequency-domain input-output data to identify continuous-time and discrete-time transfer functions, process odels, and state-space models. The toolbox provides maximum likelihood, prediction-error minimization (PEM), subspace system identification, and other identification techniques.For nonlinear system dynamics, you can estimate Hammerstein-Weiner models and nonlinear ARX models with wavelet network, tree-partition, and sigmoid network nonlinearities. The toolbox performs grey-box system identification for estimating parameters of a user-defined model. You can use the identified model for prediction of system response and for simulation in Simulink. The toolbox also lets you model time-series data and perform time-series forecasting. The more important content in this book is the next:* Transfer function, process model, and state-space model identification using time-domain and frequency-domain response data* Autoregressive (ARX, ARMAX), Box-Jenkins, and Output-Error model estimation using maximum likelihood, prediction-error minimization(PEM), and subspace system identification techniques * Time-series modeling (AR, ARMA, ARIMA) and forecasting* Identification of nonlinear ARX models and Hammerstein-Weiner models with input-output nonlinearities such as saturation and dead zone* Linear and nonlinear grey-box system identification for estimation of user-defined models* Delay estimation, detrending, filtering, resampling, and reconstruction of missing data

Subspace Identification for Linear Systems

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Author : Peter van Overschee
Publisher : Springer Science & Business Media
ISBN 13 : 1461304652
Total Pages : 263 pages
Book Rating : 4.4/5 (613 download)

Book Synopsis Subspace Identification for Linear Systems by : Peter van Overschee

Download or read book Subspace Identification for Linear Systems written by Peter van Overschee and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: Subspace Identification for Linear Systems focuses on the theory, implementation and applications of subspace identification algorithms for linear time-invariant finite- dimensional dynamical systems. These algorithms allow for a fast, straightforward and accurate determination of linear multivariable models from measured input-output data. The theory of subspace identification algorithms is presented in detail. Several chapters are devoted to deterministic, stochastic and combined deterministic-stochastic subspace identification algorithms. For each case, the geometric properties are stated in a main 'subspace' Theorem. Relations to existing algorithms and literature are explored, as are the interconnections between different subspace algorithms. The subspace identification theory is linked to the theory of frequency weighted model reduction, which leads to new interpretations and insights. The implementation of subspace identification algorithms is discussed in terms of the robust and computationally efficient RQ and singular value decompositions, which are well-established algorithms from numerical linear algebra. The algorithms are implemented in combination with a whole set of classical identification algorithms, processing and validation tools in Xmath's ISID, a commercially available graphical user interface toolbox. The basic subspace algorithms in the book are also implemented in a set of Matlab files accompanying the book. An application of ISID to an industrial glass tube manufacturing process is presented in detail, illustrating the power and user-friendliness of the subspace identification algorithms and of their implementation in ISID. The identified model allows for an optimal control of the process, leading to a significant enhancement of the production quality. The applicability of subspace identification algorithms in industry is further illustrated with the application of the Matlab files to ten practical problems. Since all necessary data and Matlab files are included, the reader can easily step through these applications, and thus get more insight in the algorithms. Subspace Identification for Linear Systems is an important reference for all researchers in system theory, control theory, signal processing, automization, mechatronics, chemical, electrical, mechanical and aeronautical engineering.

System Identification

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Author : Rik Pintelon
Publisher : John Wiley & Sons
ISBN 13 : 0471660957
Total Pages : 644 pages
Book Rating : 4.4/5 (716 download)

Book Synopsis System Identification by : Rik Pintelon

Download or read book System Identification written by Rik Pintelon and published by John Wiley & Sons. This book was released on 2004-04-05 with total page 644 pages. Available in PDF, EPUB and Kindle. Book excerpt: Electrical Engineering System Identification A Frequency Domain Approach How does one model a linear dynamic system from noisy data? This book presents a general approach to this problem, with both practical examples and theoretical discussions that give the reader a sound understanding of the subject and of the pitfalls that might occur on the road from raw data to validated model. The emphasis is on robust methods that can be used with a minimum of user interaction. Readers in many fields of engineering will gain knowledge about: * Choice of experimental setup and experiment design * Automatic characterization of disturbing noise * Generation of a good plant model * Detection, qualification, and quantification of nonlinear distortions * Identification of continuous- and discrete-time models * Improved model validation tools and from the theoretical side about: * System identification * Interrelations between time- and frequency-domain approaches * Stochastic properties of the estimators * Stochastic analysis System Identification: A Frequency Domain Approach is written for practicing engineers and scientists who do not want to delve into mathematical details of proofs. Also, it is written for researchers who wish to learn more about the theoretical aspects of the proofs. Several of the introductory chapters are suitable for undergraduates. Each chapter begins with an abstract and ends with exercises, and examples are given throughout.

Modeling of Dynamic Systems

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Author : Lennart Ljung
Publisher : Prentice Hall
ISBN 13 : 9780135970973
Total Pages : 0 pages
Book Rating : 4.9/5 (79 download)

Book Synopsis Modeling of Dynamic Systems by : Lennart Ljung

Download or read book Modeling of Dynamic Systems written by Lennart Ljung and published by Prentice Hall. This book was released on 1994 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written by a recognized authority in the field of identification and control, this book draws together into a single volume the important aspects of system identification AND physical modelling. KEY TOPICS: Explores techniques used to construct mathematical models of systems based on knowledge from physics, chemistry, biology, etc. (e.g., techniques with so called bond-graphs, as well those which use computer algebra for the modeling work). Explains system identification techniques used to infer knowledge about the behavior of dynamic systems based on observations of the various input and output signals that are available for measurement. Shows how both types of techniques need to be applied in any given practical modeling situation. Considers applications, primarily simulation. MARKET: For practicing engineers who are faced with problems of modeling.

Filtering and System Identification

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Author : Michel Verhaegen
Publisher : Cambridge University Press
ISBN 13 : 1139465023
Total Pages : 395 pages
Book Rating : 4.1/5 (394 download)

Book Synopsis Filtering and System Identification by : Michel Verhaegen

Download or read book Filtering and System Identification written by Michel Verhaegen and published by Cambridge University Press. This book was released on 2007-04-26 with total page 395 pages. Available in PDF, EPUB and Kindle. Book excerpt: Filtering and system identification are powerful techniques for building models of complex systems. This 2007 book discusses the design of reliable numerical methods to retrieve missing information in models derived using these techniques. Emphasis is on the least squares approach as applied to the linear state-space model, and problems of increasing complexity are analyzed and solved within this framework, starting with the Kalman filter and concluding with the estimation of a full model, noise statistics and state estimator directly from the data. Key background topics, including linear matrix algebra and linear system theory, are covered, followed by different estimation and identification methods in the state-space model. With end-of-chapter exercises, MATLAB simulations and numerous illustrations, this book will appeal to graduate students and researchers in electrical, mechanical and aerospace engineering. It is also useful for practitioners. Additional resources for this title, including solutions for instructors, are available online at www.cambridge.org/9780521875127.

Nonlinear System Identification

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Author : Stephen A. Billings
Publisher : John Wiley & Sons
ISBN 13 : 1118535553
Total Pages : 611 pages
Book Rating : 4.1/5 (185 download)

Book Synopsis Nonlinear System Identification by : Stephen A. Billings

Download or read book Nonlinear System Identification written by Stephen A. Billings and published by John Wiley & Sons. This book was released on 2013-07-29 with total page 611 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains describes a comprehensive framework for the identification and analysis of nonlinear dynamic systems in the time, frequency, and spatio-temporal domains. This book is written with an emphasis on making the algorithms accessible so that they can be applied and used in practice. Includes coverage of: The NARMAX (nonlinear autoregressive moving average with exogenous inputs) model The orthogonal least squares algorithm that allows models to be built term by term where the error reduction ratio reveals the percentage contribution of each model term Statistical and qualitative model validation methods that can be applied to any model class Generalised frequency response functions which provide significant insight into nonlinear behaviours A completely new class of filters that can move, split, spread, and focus energy The response spectrum map and the study of sub harmonic and severely nonlinear systems Algorithms that can track rapid time variation in both linear and nonlinear systems The important class of spatio-temporal systems that evolve over both space and time Many case study examples from modelling space weather, through identification of a model of the visual processing system of fruit flies, to tracking causality in EEG data are all included to demonstrate how easily the methods can be applied in practice and to show the insight that the algorithms reveal even for complex systems NARMAX algorithms provide a fundamentally different approach to nonlinear system identification and signal processing for nonlinear systems. NARMAX methods provide models that are transparent, which can easily be analysed, and which can be used to solve real problems. This book is intended for graduates, postgraduates and researchers in the sciences and engineering, and also for users from other fields who have collected data and who wish to identify models to help to understand the dynamics of their systems.

System Identification

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Author : Karel J. Keesman
Publisher : Springer Science & Business Media
ISBN 13 : 0857295225
Total Pages : 334 pages
Book Rating : 4.8/5 (572 download)

Book Synopsis System Identification by : Karel J. Keesman

Download or read book System Identification written by Karel J. Keesman and published by Springer Science & Business Media. This book was released on 2011-05-16 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: System Identification shows the student reader how to approach the system identification problem in a systematic fashion. The process is divided into three basic steps: experimental design and data collection; model structure selection and parameter estimation; and model validation, each of which is the subject of one or more parts of the text. Following an introduction on system theory, particularly in relation to model representation and model properties, the book contains four parts covering: • data-based identification – non-parametric methods for use when prior system knowledge is very limited; • time-invariant identification for systems with constant parameters; • time-varying systems identification, primarily with recursive estimation techniques; and • model validation methods. A fifth part, composed of appendices, covers the various aspects of the underlying mathematics needed to begin using the text. The book uses essentially semi-physical or gray-box modeling methods although data-based, transfer-function system descriptions are also introduced. The approach is problem-based rather than rigorously mathematical. The use of finite input–output data is demonstrated for frequency- and time-domain identification in static, dynamic, linear, nonlinear, time-invariant and time-varying systems. Simple examples are used to show readers how to perform and emulate the identification steps involved in various control design methods with more complex illustrations derived from real physical, chemical and biological applications being used to demonstrate the practical applicability of the methods described. End-of-chapter exercises (for which a downloadable instructors’ Solutions Manual is available from fill in URL here) will both help students to assimilate what they have learned and make the book suitable for self-tuition by practitioners looking to brush up on modern techniques. Graduate and final-year undergraduate students will find this text to be a practical and realistic course in system identification that can be used for assessing the processes of a variety of engineering disciplines. System Identification will help academic instructors teaching control-related to give their students a good understanding of identification methods that can be used in the real world without the encumbrance of undue mathematical detail.

System Identification

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Author : Lennart Ljung
Publisher :
ISBN 13 :
Total Pages : 609 pages
Book Rating : 4.:/5 (797 download)

Book Synopsis System Identification by : Lennart Ljung

Download or read book System Identification written by Lennart Ljung and published by . This book was released on 1999 with total page 609 pages. Available in PDF, EPUB and Kindle. Book excerpt: