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Author: Jer-Nan Juang
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 424
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Book Description
System identification is the process of developing or improving a mathematical representation of a physical system using experimental data. Over the past decade, several system identification techniques have been developed within different disciplines. This text/reference brings together the significant advances over the past decade into a single unified source -- with common mathematical notation that will enable readers from a variety of engineering areas -- e.g., aerospace, electrical, civil, and mechanical engineering --to apply system identification to engineering systems. Focuses on the three types of identification in engineering structures -- modal parameter identification; structural-model parameter identification; and control-model identification. For researchers and engineers, students, and teachers in vibrations, controls and system identification.
Author: Jer-Nan Juang
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 424
Get Book Here
Book Description
System identification is the process of developing or improving a mathematical representation of a physical system using experimental data. Over the past decade, several system identification techniques have been developed within different disciplines. This text/reference brings together the significant advances over the past decade into a single unified source -- with common mathematical notation that will enable readers from a variety of engineering areas -- e.g., aerospace, electrical, civil, and mechanical engineering --to apply system identification to engineering systems. Focuses on the three types of identification in engineering structures -- modal parameter identification; structural-model parameter identification; and control-model identification. For researchers and engineers, students, and teachers in vibrations, controls and system identification.
Author: Karel J. Keesman
Publisher: Springer Science & Business Media
ISBN: 0857295225
Category : Technology & Engineering
Languages : en
Pages : 334
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Book Description
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.
Author: Tohru Katayama
Publisher: Springer Science & Business Media
ISBN: 184628158X
Category : Technology & Engineering
Languages : en
Pages : 400
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Book Description
An in-depth introduction to subspace methods for system identification in discrete-time linear systems thoroughly augmented with advanced and novel results, this text is structured into three parts. Part I deals with the mathematical preliminaries: numerical linear algebra; system theory; stochastic processes; and Kalman filtering. Part II explains realization theory as applied to subspace identification. Stochastic realization results based on spectral factorization and Riccati equations, and on canonical correlation analysis for stationary processes are included. Part III demonstrates the closed-loop application of subspace identification methods. Subspace Methods for System Identification is an excellent reference for researchers and a useful text for tutors and graduate students involved in control and signal processing courses. It can be used for self-study and will be of interest to applied scientists or engineers wishing to use advanced methods in modeling and identification of complex systems.
Author: Rik Pintelon
Publisher: John Wiley & Sons
ISBN: 0471660957
Category : Science
Languages : en
Pages : 644
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Book Description
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.
Author: Magdi S. Mahmoud
Publisher: Springer Science & Business Media
ISBN: 1447128796
Category : Technology & Engineering
Languages : en
Pages : 576
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Book Description
Applied Control System Design examines several methods for building up systems models based on real experimental data from typical industrial processes and incorporating system identification techniques. The text takes a comparative approach to the models derived in this way judging their suitability for use in different systems and under different operational circumstances. A broad spectrum of control methods including various forms of filtering, feedback and feedforward control is applied to the models and the guidelines derived from the closed-loop responses are then composed into a concrete self-tested recipe to serve as a check-list for industrial engineers or control designers. System identification and control design are given equal weight in model derivation and testing to reflect their equality of importance in the proper design and optimization of high-performance control systems. Readers’ assimilation of the material discussed is assisted by the provision of problems and examples. Most of these exercises use MATLAB® to make computation and visualization more straightforward. Applied Control System Design will be of interest to academic researchers for its comparison of different systems models and their response to different control methods and will assist graduate students in learning the practical necessities of advanced control system design. The consistent reference to real systems coupled with self-learning tools will assist control practitioners who wish to keep up to date with the latest control design ideas.
Author: Johan Schoukens
Publisher: John Wiley & Sons
ISBN: 1118218507
Category : Technology & Engineering
Languages : en
Pages : 285
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Book Description
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.
Author: Thomas McAvinew
Publisher: ISA
ISBN: 9781556178962
Category : Technology & Engineering
Languages : en
Pages : 282
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Book Description
Offers symbols and identification that are commonly used throughout the process industries. This book contains sample P&ID and numerous examples of symbols and tagging concepts. It is suitable for instrumentation specialists.
Author: Torsten Söderström
Publisher:
ISBN:
Category : Science
Languages : en
Pages : 646
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Book Description
A textbook designed for senior undergraduate and graduate level classroom courses on system identification. Examples and problems. Annotation copyrighted by Book News, Inc., Portland, OR
Author: Lennart Ljung
Publisher: Pearson Education
ISBN: 0132440539
Category : Technology & Engineering
Languages : en
Pages : 875
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Book Description
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.
Author: Arun K. Tangirala
Publisher: CRC Press
ISBN: 143989602X
Category : Technology & Engineering
Languages : en
Pages : 881
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Book Description
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
