Regularized System Identification

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Regularized System Identification

Gianluigi Pillonetto,Tianshi Chen,Alessandro Chiuso,Giuseppe De Nicolao,Lennart Ljung
Regularized System Identification Book PDF

Author : Gianluigi Pillonetto,Tianshi Chen,Alessandro Chiuso,Giuseppe De Nicolao,Lennart Ljung
Publisher : Springer Nature
Page : 394 pages
File Size : 44,6 Mb
Release : 2022-05-13
Category : Computers
ISBN : 9783030958602

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Regularized System Identification by Gianluigi Pillonetto,Tianshi Chen,Alessandro Chiuso,Giuseppe De Nicolao,Lennart Ljung Pdf

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.

Nonlinear System Identification

Oliver Nelles
Nonlinear System Identification Book PDF

Author : Oliver Nelles
Publisher : Springer Nature
Page : 1235 pages
File Size : 40,6 Mb
Release : 2020-09-09
Category : Science
ISBN : 9783030474393

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Nonlinear System Identification by Oliver Nelles Pdf

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.

Linear Parameter-Varying System Identification

Paulo Lopes dos Santos
Linear Parameter Varying System Identification Book PDF

Author : Paulo Lopes dos Santos
Publisher : World Scientific
Page : 402 pages
File Size : 55,9 Mb
Release : 2012
Category : Mathematics
ISBN : 9789814355452

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Linear Parameter-Varying System Identification by Paulo Lopes dos Santos Pdf

This review volume reports the state-of-the-art in Linear Parameter Varying (LPV) system identification. Written by world renowned researchers, the book contains twelve chapters, focusing on the most recent LPV identification methods for both discrete-time and continuous-time models, using different approaches such as optimization methods for input/output LPV models Identification, set membership methods, optimization methods and subspace methods for state-space LPV models identification and orthonormal basis functions methods. Since there is a strong connection between LPV systems, hybrid switching systems and piecewise affine models, identification of hybrid switching systems and piecewise affine systems will be considered as well.

System Identification (SYSID '03)

Paul Van Den Hof,Bo Wahlberg,Siep Weiland
System Identification SYSID 03 Book PDF

Author : Paul Van Den Hof,Bo Wahlberg,Siep Weiland
Publisher : Elsevier
Page : 2080 pages
File Size : 49,7 Mb
Release : 2004-06-29
Category : Science
ISBN : 0080437095

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System Identification (SYSID '03) by Paul Van Den Hof,Bo Wahlberg,Siep Weiland Pdf

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

Arun K. Tangirala
Principles of System Identification Book PDF

Author : Arun K. Tangirala
Publisher : CRC Press
Page : 908 pages
File Size : 40,9 Mb
Release : 2018-10-08
Category : Technology & Engineering
ISBN : 9781439896020

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Principles of System Identification by Arun K. Tangirala Pdf

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

Regularization, Optimization, Kernels, and Support Vector Machines

Johan A.K. Suykens,Marco Signoretto,Andreas Argyriou
Regularization Optimization Kernels and Support Vector Machines Book PDF

Author : Johan A.K. Suykens,Marco Signoretto,Andreas Argyriou
Publisher : CRC Press
Page : 528 pages
File Size : 54,7 Mb
Release : 2014-10-23
Category : Computers
ISBN : 9781482241396

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Regularization, Optimization, Kernels, and Support Vector Machines by Johan A.K. Suykens,Marco Signoretto,Andreas Argyriou Pdf

Regularization, Optimization, Kernels, and Support Vector Machines offers a snapshot of the current state of the art of large-scale machine learning, providing a single multidisciplinary source for the latest research and advances in regularization, sparsity, compressed sensing, convex and large-scale optimization, kernel methods, and support vector machines. Consisting of 21 chapters authored by leading researchers in machine learning, this comprehensive reference: Covers the relationship between support vector machines (SVMs) and the Lasso Discusses multi-layer SVMs Explores nonparametric feature selection, basis pursuit methods, and robust compressive sensing Describes graph-based regularization methods for single- and multi-task learning Considers regularized methods for dictionary learning and portfolio selection Addresses non-negative matrix factorization Examines low-rank matrix and tensor-based models Presents advanced kernel methods for batch and online machine learning, system identification, domain adaptation, and image processing Tackles large-scale algorithms including conditional gradient methods, (non-convex) proximal techniques, and stochastic gradient descent Regularization, Optimization, Kernels, and Support Vector Machines is ideal for researchers in machine learning, pattern recognition, data mining, signal processing, statistical learning, and related areas.

Hybrid System Identification

Fabien Lauer,Gérard Bloch
Hybrid System Identification Book PDF

Author : Fabien Lauer,Gérard Bloch
Publisher : Springer
Page : 253 pages
File Size : 52,5 Mb
Release : 2018-10-04
Category : Technology & Engineering
ISBN : 9783030001933

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Hybrid System Identification by Fabien Lauer,Gérard Bloch Pdf

​Hybrid System Identification helps readers to build mathematical models of dynamical systems switching between different operating modes, from their experimental observations. It provides an overview of the interaction between system identification, machine learning and pattern recognition fields in explaining and analysing hybrid system identification. It emphasises the optimization and computational complexity issues that lie at the core of the problems considered and sets them aside from standard system identification problems. The book presents practical methods that leverage this complexity, as well as a broad view of state-of-the-art machine learning methods. The authors illustrate the key technical points using examples and figures to help the reader understand the material. The book includes an in-depth discussion and computational analysis of hybrid system identification problems, moving from the basic questions of the definition of hybrid systems and system identification to methods of hybrid system identification and the estimation of switched linear/affine and piecewise affine models. The authors also give an overview of the various applications of hybrid systems, discuss the connections to other fields, and describe more advanced material on recursive, state-space and nonlinear hybrid system identification. Hybrid System Identification includes a detailed exposition of major methods, which allows researchers and practitioners to acquaint themselves rapidly with state-of-the-art tools. The book is also a sound basis for graduate and undergraduate students studying this area of control, as the presentation and form of the book provides the background and coverage necessary for a full understanding of hybrid system identification, whether the reader is initially familiar with system identification related to hybrid systems or not.

System Identification

Torsten Söderström,Petre Stoica
System Identification Book PDF

Author : Torsten Söderström,Petre Stoica
Publisher : Unknown
Page : 646 pages
File Size : 42,5 Mb
Release : 1989
Category : Science
ISBN : UOM:39015028285982

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System Identification by Torsten Söderström,Petre Stoica Pdf

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

System Identification

Rik Pintelon,Johan Schoukens
System Identification Book PDF

Author : Rik Pintelon,Johan Schoukens
Publisher : John Wiley & Sons
Page : 644 pages
File Size : 49,8 Mb
Release : 2004-04-05
Category : Science
ISBN : 9780471660958

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System Identification by Rik Pintelon,Johan Schoukens Pdf

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.

Identification of Linear Systems

J. Schoukens,R. Pintelon
Identification of Linear Systems Book PDF

Author : J. Schoukens,R. Pintelon
Publisher : Elsevier
Page : 353 pages
File Size : 47,9 Mb
Release : 2014-06-28
Category : Science
ISBN : 9780080912561

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Identification of Linear Systems by J. Schoukens,R. Pintelon Pdf

This book concentrates on the problem of accurate modeling of linear systems. It presents a thorough description of a method of modeling a linear dynamic invariant system by its transfer function. The first two chapters provide a general introduction and review for those readers who are unfamiliar with identification theory so that they have a sufficient background knowledge for understanding the methods described later. The main body of the book looks at the basic method used by the authors to estimate the parameter of the transfer function, how it is possible to optimize the excitation signals. Further chapters extend the estimation method proposed. Applications are then discussed and the book concludes with practical guidelines which illustrate the method and offer some rules-of-thumb.

Ubiquitous Intelligent Systems

P. Karuppusamy,Isidoros Perikos,Fausto Pedro García Márquez
Ubiquitous Intelligent Systems Book PDF

Author : P. Karuppusamy,Isidoros Perikos,Fausto Pedro García Márquez
Publisher : Springer Nature
Page : 741 pages
File Size : 40,8 Mb
Release : 2021-10-08
Category : Technology & Engineering
ISBN : 9789811636752

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Ubiquitous Intelligent Systems by P. Karuppusamy,Isidoros Perikos,Fausto Pedro García Márquez Pdf

This book features a collection of high-quality, peer-reviewed papers presented at International Conference on Ubiquitous Intelligent Systems (ICUIS 2021) organized by Shree Venkateshwara Hi-Tech Engineering College, Tamil Nadu, India, during April 16–17, 2021. The book covers topics such as cloud computing, mobile computing and networks, embedded computing frameworks, modeling and analysis of ubiquitous information systems, communication networking models, big data models and applications, ubiquitous information processing systems, next-generation ubiquitous networks and protocols, advanced intelligent systems, Internet of things, wireless communication and storage networks, intelligent information retrieval techniques, AI-based intelligent information visualization techniques, cognitive informatics, smart automation systems, healthcare informatics and bioinformatics models, security and privacy of intelligent information systems, and smart distributed information systems.

Adaptive Identification of Acoustic Multichannel Systems Using Sparse Representations

Karim Helwani
Adaptive Identification of Acoustic Multichannel Systems Using Sparse Representations Book PDF

Author : Karim Helwani
Publisher : Springer
Page : 113 pages
File Size : 52,7 Mb
Release : 2014-07-25
Category : Technology & Engineering
ISBN : 9783319089546

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Adaptive Identification of Acoustic Multichannel Systems Using Sparse Representations by Karim Helwani Pdf

This book treats the topic of extending the adaptive filtering theory in the context of massive multichannel systems by taking into account a priori knowledge of the underlying system or signal. The starting point is exploiting the sparseness in acoustic multichannel system in order to solve the non-uniqueness problem with an efficient algorithm for adaptive filtering that does not require any modification of the loudspeaker signals. The book discusses in detail the derivation of general sparse representations of acoustic MIMO systems in signal or system dependent transform domains. Efficient adaptive filtering algorithms in the transform domains are presented and the relation between the signal- and the system-based sparse representations is emphasized. Furthermore, the book presents a novel approach to spatially preprocess the loudspeaker signals in a full-duplex communication system. The idea of the preprocessing is to prevent the echoes from being captured by the microphone array in order to support the AEC system. The preprocessing stage is given as an exemplarily application of a novel unified framework for the synthesis of sound figures. Finally, a multichannel system for the acoustic echo suppression is presented that can be used as a postprocessing stage for removing residual echoes. As first of its kind, it extracts the near-end signal from the microphone signal with a distortionless constraint and without requiring a double-talk detector.

Regularization, Optimization, Kernels, and Support Vector Machines

Johan A.K. Suykens,Marco Signoretto,Andreas Argyriou
Regularization Optimization Kernels and Support Vector Machines Book PDF

Author : Johan A.K. Suykens,Marco Signoretto,Andreas Argyriou
Publisher : CRC Press
Page : 522 pages
File Size : 46,7 Mb
Release : 2014-10-23
Category : Computers
ISBN : 9781482241402

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Regularization, Optimization, Kernels, and Support Vector Machines by Johan A.K. Suykens,Marco Signoretto,Andreas Argyriou Pdf

Regularization, Optimization, Kernels, and Support Vector Machines offers a snapshot of the current state of the art of large-scale machine learning, providing a single multidisciplinary source for the latest research and advances in regularization, sparsity, compressed sensing, convex and large-scale optimization, kernel methods, and support vecto

Artificial Neural Networks and Machine Learning -- ICANN 2014

Stefan Wermter,Cornelius Weber,Wlodzislaw Duch,Timo Honkela,Petia Koprinkova-Hristova,Sven Magg,Günther Palm,Allessandro E.P. Villa
Artificial Neural Networks and Machine Learning ICANN 2014 Book PDF

Author : Stefan Wermter,Cornelius Weber,Wlodzislaw Duch,Timo Honkela,Petia Koprinkova-Hristova,Sven Magg,Günther Palm,Allessandro E.P. Villa
Publisher : Springer
Page : 852 pages
File Size : 52,9 Mb
Release : 2014-08-18
Category : Computers
ISBN : 9783319111797

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Artificial Neural Networks and Machine Learning -- ICANN 2014 by Stefan Wermter,Cornelius Weber,Wlodzislaw Duch,Timo Honkela,Petia Koprinkova-Hristova,Sven Magg,Günther Palm,Allessandro E.P. Villa Pdf

The book constitutes the proceedings of the 24th International Conference on Artificial Neural Networks, ICANN 2014, held in Hamburg, Germany, in September 2014. The 107 papers included in the proceedings were carefully reviewed and selected from 173 submissions. The focus of the papers is on following topics: recurrent networks; competitive learning and self-organisation; clustering and classification; trees and graphs; human-machine interaction; deep networks; theory; reinforcement learning and action; vision; supervised learning; dynamical models and time series; neuroscience; and applications.

Regularized Image Reconstruction in Parallel MRI with MATLAB

Joseph Suresh Paul,Raji Susan Mathew
Regularized Image Reconstruction in Parallel MRI with MATLAB Book PDF

Author : Joseph Suresh Paul,Raji Susan Mathew
Publisher : CRC Press
Page : 306 pages
File Size : 44,7 Mb
Release : 2019-11-05
Category : Medical
ISBN : 9781351029254

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Regularized Image Reconstruction in Parallel MRI with MATLAB by Joseph Suresh Paul,Raji Susan Mathew Pdf

Regularization becomes an integral part of the reconstruction process in accelerated parallel magnetic resonance imaging (pMRI) due to the need for utilizing the most discriminative information in the form of parsimonious models to generate high quality images with reduced noise and artifacts. Apart from providing a detailed overview and implementation details of various pMRI reconstruction methods, Regularized image reconstruction in parallel MRI with MATLAB examples interprets regularized image reconstruction in pMRI as a means to effectively control the balance between two specific types of error signals to either improve the accuracy in estimation of missing samples, or speed up the estimation process. The first type corresponds to the modeling error between acquired and their estimated values. The second type arises due to the perturbation of k-space values in autocalibration methods or sparse approximation in the compressed sensing based reconstruction model. Features: Provides details for optimizing regularization parameters in each type of reconstruction. Presents comparison of regularization approaches for each type of pMRI reconstruction. Includes discussion of case studies using clinically acquired data. MATLAB codes are provided for each reconstruction type. Contains method-wise description of adapting regularization to optimize speed and accuracy. This book serves as a reference material for researchers and students involved in development of pMRI reconstruction methods. Industry practitioners concerned with how to apply regularization in pMRI reconstruction will find this book most useful.