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.
This book contains the proceedings of a workshop to celebrate Lennart Ljung's 60ths birthday. Lennart Ljung is professor in control theory at Linköping University and one of the leading researches in System identification. The Symposium speakers are Albert Benveniste, Peter Caines, Michel Gevers, Torkel Glad, Keith Glover, Graham Goodwin, Lei Guo, Boris Polyak, Torsten Söderström, Bo Wahlberg and Karl Johan Åström. They have all worked together with Lennart Ljung on various aspects of system identification. Together they illustrate the development, breadth and possible future directions of the subject.
This is a textbook designed for an advanced course in control theory. Currently most textbooks on the subject either looks at "multivariate" systems or "non-linear" systems. However, Control Theory is the only textbook available that covers both. It explains current developments in these two types of control techniques, and looks at tools for computer-aided design, for example Matlab and its toolboxes. To make full use of computer design tools, a good understanding of their theoretical basis is necessary, and to enable this, the book presents relevant mathematics clearly and simply. The practical limits of control systems are explored, and the relevance of these to control design are discussed. Control Theory is an ideal textbook for final-year undergraduate and postgraduate courses, and the student will be helped by a series of exercises at the end of each chapter. Professional engineers will also welcome it as a core reference.
Signal Processing" is a comprehensive treatment of modern signal processing theory and its main applications. The authors provide a unique perspective, combining classic methods based on transforms and filter construction with analytical methods based on explicit signal models. All algorithms and examples are illustrated with reproducible Matlab code. The first part of the book deals with classic non-parametric methods based on filters and transforms. A key here is the Discrete Fourier Transform and its relation to the Continuous Fourier Transform. Further, signals that can be described as stationary stochastic processes are treated, and common methods to estimate their covariance function and spectrum are described. This part ends with a description of different strategies for filtering of signals in the time and frequency domain. Typical application areas are signal conditioning (noise attenuation) and spectral analysis. The second part describes parametric model-based methods. Different standard parametric models and their relation are surveyed, and methods to estimate parameters from measurements are presented. For example, one chapter describes adaptive filtering theory, where the goal is to estimate these parameters recursively in time for time-varying signal models. Important application areas here are prediction, signal conditioning and spectral analysis. Signal conditioning and prediction are also the key applications of the Wiener and Kalman filters, which are treated in separate chapters. The book homepage contains more information and links to access the Matlab functions, data sets and examples used in the book: www.studentlitteratur.se/signalprocessing under the flap Extramaterial.
This open access book provides a comprehensive treatment of recent developments in kernel-based identification that are of interest to anyone engaged in learning dynamic systems from data. The reader is led step by step into understanding of a novel paradigm that leverages the power of machine learning without losing sight of the system-theoretical principles of black-box identification. The authors’ reformulation of the identification problem in the light of regularization theory not only offers new insight on classical questions, but paves the way to new and powerful algorithms for a variety of linear and nonlinear problems. Regression methods such as regularization networks and support vector machines are the basis of techniques that extend the function-estimation problem to the estimation of dynamic models. Many examples, also from real-world applications, illustrate the comparative advantages of the new nonparametric approach with respect to classic parametric prediction error methods. The challenges it addresses lie at the intersection of several disciplines so Regularized System Identification will be of interest to a variety of researchers and practitioners in the areas of control systems, machine learning, statistics, and data science. This is an open access book.
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