Appropriate for courses in System Identification. This book is a comprehensive and coherent description of the theory, methodology and practice of System Identification--the science of building mathematical models of dynamic systems by observing input/output data. It puts the user in focus, giving the necessary background to understand theoretical foundation and emphasizing the practical aspects of the options and choices that face the user. The Second Edition has been updated to include material on subspace methods, non-linear black box models--such as neural networks--and methods that use frequency domain data.
System Identification: Theory for the User (2nd Edition)
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Describes the diverse area of identification algorithms within a coherent framework. An account of underlying principles, main theoretical results as well as algorithms and practical aspects. For Sr/Grad course or systems identification in EE, ME, CHE (usually taught by controls and systems people).
--This text refers to an out of print or unavailable edition of this title.
From the Back Cover
65669-4
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.
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Most Helpful Customer Reviews
12 of 13 people found the following review helpful:
2.0 out of 5 stars
Poorly written book for the engineer. Might be useful for a mathemtician,
By scotthew "bigmbooks" (Columbia, MD United States) - See all my reviews
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This review is from: System Identification: Theory for the User (2nd Edition) (Hardcover)
Let me be the first to slam this book as having little practical use. The author uses a very unconventional, inconsistent notation that renders this book virtually unreadable. This is coming from somebody who has taken various courses in probability/stochastic processes, system dynamics, controls, and Kalman filtering.
The "theory" part in the title is dead on, but what about the "for the user" part? What user is this referring to, an ivory tower professor? Most of the problems at the end of each chapter begin with "prove", which in my opinion makes this a math book and not an engineering text. A practical example never surfaces in the text.
14 of 16 people found the following review helpful:
4.0 out of 5 stars
A Complete Text for System Identification,
By Chris S. Edrington (Rolla, MO United States) - See all my reviews
This review is from: System Identification: Theory for the User (2nd Edition) (Hardcover)
This book is very through in presenting methods of system identification...mathematically. However as with most good mathematical oriented books there are not a lot of practical design type problems. One other mark aginst the book is that similar notation is used for different topics, which can sometimes be a bit confusing. One thing I might recommend, to a potential buyer is that you take a course in random process, and possess a through understanding of signals and transforms. All in all though if a little more meat in terms of practicality were added I'd rate this book a five.
8 of 9 people found the following review helpful:
3.0 out of 5 stars
Notational Inconsistency killed this book,
By
This review is from: System Identification: Theory for the User (2nd Edition) (Hardcover)
I have two system identification books on my table right now: One is by Prof Ljung and the other is 'Time Series: Theory and Methods' by Profs. Brockwell and Davis, and I must say that I refer to the latter much more often than the former. The primary reason for this is that Prof. Ljung's book uses notation very inconsistently. This makes for very difficult reading. On the other hand 'Time Series: T&M' is consistent and an excellent resource. Both books are of an advanced nature, suitable for graduate students. Again, 'Time Series T&M' is more rigorous.
Nonetheless, if you plan on using Matlab's Sys Id toolbox, I would recommend Prof. Ljung's book. He wrote the toolbox, and he refers heavily in the Matlab documentation to the book.
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Inside This Book
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Key Phrases - Statistically Improbable Phrases (SIPs):
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parameter estimation methods, signal spectra, output error model structure, estimating state space models, presampling filters, closed loop data, model structure selection, extended observability matrix, intersample behavior, spectral analysis estimate, associated prediction errors, open loop operation, closed loop identification, recursive identification algorithms, false local minima, prediction error method, limiting estimate, minimizing argument, local identifiability, multivariable case, model parametrization, slow disturbances, robust norms, transfer function estimate, multistage methods Key Phrases - Capitalized Phrases (CAPs): (learn more)
Models of Linear Time-Invariant Systems, Time-Invariant Linear Systems, Experiment Design, Computing the Estimate, Time Figure, Proof of Theorem, Recursive Estimation Methods, Best Fits, Family of Transfer-Function Models, Identifiability of Black-Box Multivariable Model Structures, Some Applications, Asymptotic Distribution of Parameter Estimates, Use Problem, Proof of Lemma, Comparing Model Structures, Some Formal Aspects, Nonlinear Black-Box Models, Frequency-Domain Methods, Choice of Norm, Choice of Identification Criterion, Fit Linear Models, The Choice of Updating Step, Basic Principles, Frequency-Domain Description of the Limit Model, Consider Problem Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
parameter estimation methods, signal spectra, output error model structure, estimating state space models, presampling filters, closed loop data, model structure selection, extended observability matrix, intersample behavior, spectral analysis estimate, associated prediction errors, open loop operation, closed loop identification, recursive identification algorithms, false local minima, prediction error method, limiting estimate, minimizing argument, local identifiability, multivariable case, model parametrization, slow disturbances, robust norms, transfer function estimate, multistage methods Key Phrases - Capitalized Phrases (CAPs): (learn more)
Models of Linear Time-Invariant Systems, Time-Invariant Linear Systems, Experiment Design, Computing the Estimate, Time Figure, Proof of Theorem, Recursive Estimation Methods, Best Fits, Family of Transfer-Function Models, Identifiability of Black-Box Multivariable Model Structures, Some Applications, Asymptotic Distribution of Parameter Estimates, Use Problem, Proof of Lemma, Comparing Model Structures, Some Formal Aspects, Nonlinear Black-Box Models, Frequency-Domain Methods, Choice of Norm, Choice of Identification Criterion, Fit Linear Models, The Choice of Updating Step, Basic Principles, Frequency-Domain Description of the Limit Model, Consider Problem Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
100
books
cite this book:
See all 100 books citing this book
- System Identification 2003 (IPV - IFAC Proceedings Volume) by Paul Van Den Hof on 5 pages
- Proceedings of the 15th Ifac World Congress on the International Federation of Automatic Control: Identification (IFAC Proceedings Volumes) by E. F. Camacho on 4 pages
- Adaptation and Learning in Control and Signal Processing 2001 (IFAC Proceedings Volumes) by S. Bittanti on 4 pages
- Model-Based Signal Processing (Adaptive and Learning Systems for Signal Processing, Communications and Control Series) by James V. Candy in Front Matter, page 482, and page 628
- Computer Intensive Methods in Control and Signal Processing:: The Curse of Dimensionality by Kevin Warwick on page 167, and page 193
See all 100 books citing this book
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