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Regularized System Identification


Regularized System Identification
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Regularized System Identification


Regularized System Identification
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Author : Gianluigi Pillonetto
language : en
Publisher: Springer Nature
Release Date : 2022-05-13

Regularized System Identification written by Gianluigi Pillonetto and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-13 with Computers categories.


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.



System Identification


System Identification
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Author : Torsten Söderström
language : en
Publisher:
Release Date : 1989

System Identification written by Torsten Söderström and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1989 with Science categories.


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



Nonlinear System Identification


Nonlinear System Identification
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Author : Oliver Nelles
language : en
Publisher: Springer Nature
Release Date : 2020-09-09

Nonlinear System Identification written by Oliver Nelles and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-09 with Science categories.


This book provides engineers and scientists in academia and industry with a thorough understanding of the underlying principles of nonlinear system identification. It equips them to apply the models and methods discussed to real problems with confidence, while also making them aware of potential difficulties that may arise in practice. Moreover, the book is self-contained, requiring only a basic grasp of matrix algebra, signals and systems, and statistics. Accordingly, it can also serve as an introduction to linear system identification, and provides a practical overview of the major optimization methods used in engineering. The focus is on gaining an intuitive understanding of the subject and the practical application of the techniques discussed. The book is not written in a theorem/proof style; instead, the mathematics is kept to a minimum, and the ideas covered are illustrated with numerous figures, examples, and real-world applications. In the past, nonlinear system identification was a field characterized by a variety of ad-hoc approaches, each applicable only to a very limited class of systems. With the advent of neural networks, fuzzy models, Gaussian process models, and modern structure optimization techniques, a much broader class of systems can now be handled. Although one major aspect of nonlinear systems is that virtually every one is unique, tools have since been developed that allow each approach to be applied to a wide variety of systems.



System Identification Sysid 03


System Identification Sysid 03
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Author : Paul Van Den Hof
language : en
Publisher: Elsevier
Release Date : 2004-06-29

System Identification Sysid 03 written by Paul Van Den Hof and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-06-29 with Science categories.


The scope of the symposium covers all major aspects of system identification, experimental modelling, signal processing and adaptive control, ranging from theoretical, methodological and scientific developments to a large variety of (engineering) application areas. It is the intention of the organizers to promote SYSID 2003 as a meeting place where scientists and engineers from several research communities can meet to discuss issues related to these areas. Relevant topics for the symposium program include: Identification of linear and multivariable systems, identification of nonlinear systems, including neural networks, identification of hybrid and distributed systems, Identification for control, experimental modelling in process control, vibration and modal analysis, model validation, monitoring and fault detection, signal processing and communication, parameter estimation and inverse modelling, statistical analysis and uncertainty bounding, adaptive control and data-based controller tuning, learning, data mining and Bayesian approaches, sequential Monte Carlo methods, including particle filtering, applications in process control systems, motion control systems, robotics, aerospace systems, bioengineering and medical systems, physical measurement systems, automotive systems, econometrics, transportation and communication systems *Provides the latest research on System Identification *Contains contributions written by experts in the field *Part of the IFAC Proceedings Series which provides a comprehensive overview of the major topics in control engineering.



Principles Of System Identification


Principles Of System Identification
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Author : Arun K. Tangirala
language : en
Publisher: CRC Press
Release Date : 2018-10-08

Principles Of System Identification written by Arun K. Tangirala and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-08 with Technology & Engineering categories.


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



Nonlinear System Identification


Nonlinear System Identification
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Author : Oliver Nelles
language : en
Publisher: Springer Science & Business Media
Release Date : 2001

Nonlinear System Identification written by Oliver Nelles and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Computers categories.


Written from an engineering point of view, this book covers the most common and important approaches for the identification of nonlinear static and dynamic systems. The book also provides the reader with the necessary background on optimization techniques, making it fully self-contained. The new edition includes exercises.



Filtering And System Identification


Filtering And System Identification
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Author : Michel Verhaegen
language : en
Publisher: Cambridge University Press
Release Date : 2012-07-19

Filtering And System Identification written by Michel Verhaegen and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-07-19 with Technology & Engineering categories.


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.



Cluster Analysis For Data Mining And System Identification


Cluster Analysis For Data Mining And System Identification
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Author : János Abonyi
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-08-10

Cluster Analysis For Data Mining And System Identification written by János Abonyi and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-08-10 with Mathematics categories.


The aim of this book is to illustrate that advanced fuzzy clustering algorithms can be used not only for partitioning of the data. It can also be used for visualization, regression, classification and time-series analysis, hence fuzzy cluster analysis is a good approach to solve complex data mining and system identification problems. This book is oriented to undergraduate and postgraduate and is well suited for teaching purposes.



Robust And Regularized Algorithms For Vehicle Tractive Force Prediction And Mass Estimation


Robust And Regularized Algorithms For Vehicle Tractive Force Prediction And Mass Estimation
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Author : Rhode, Stephan
language : en
Publisher: KIT Scientific Publishing
Release Date : 2018-10-01

Robust And Regularized Algorithms For Vehicle Tractive Force Prediction And Mass Estimation written by Rhode, Stephan and has been published by KIT Scientific Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-01 with Mass categories.


This work provides novel robust and regularized algorithms for parameter estimation with applications in vehicle tractive force prediction and mass estimation. Given a large record of real world data from test runs on public roads, recursive algorithms adjusted the unknown vehicle parameters under a broad variation of statistical assumptions for two linear gray-box models.



Sustainable Data Management


Sustainable Data Management
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Author : Reem Khamis Hamdan
language : en
Publisher: Springer Nature
Release Date : 2025-04-28

Sustainable Data Management written by Reem Khamis Hamdan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-28 with Computers categories.


This book dives deeper into the dynamic world of data and technology with the Sustainable Data Management and explores advanced strategies and innovative approaches to harnessing big data, leveraging communication technology, and mastering digital leadership in today's evolving business landscape. Uncover insights and techniques that propel readers organization towards sustainable success in the digital age.