Nonlinear system identification. 1. Nonlinear system parameter identification PDF Download
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Author: Robert Haber
Publisher: Springer Science & Business Media
ISBN: 9780792358565
Category : Nonlinear theories
Languages : en
Pages : 432
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Book Description
Author: Robert Haber
Publisher: Springer Science & Business Media
ISBN: 9780792358565
Category : Nonlinear theories
Languages : en
Pages : 432
Get Book
Book Description
Author: Robert Haber
Publisher: Springer
ISBN: 9780792358589
Category : Science
Languages : en
Pages : 802
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Book Description
The subject of the book is to present the modeling, parameter estimation and other aspects of the identification of nonlinear dynamic systems. The treatment is restricted to the input-output modeling approach. Because of the widespread usage of digital computers discrete time methods are preferred. Time domain parameter estimation methods are dealt with in detail, frequency domain and power spectrum procedures are described shortly. The theory is presented from the engineering point of view, and a large number of examples of case studies on the modeling and identifications of real processes illustrate the methods. Almost all processes are nonlinear if they are considered not merely in a small vicinity of the working point. To exploit industrial equipment as much as possible, mathematical models are needed which describe the global nonlinear behavior of the process. If the process is unknown, or if the describing equations are too complex, the structure and the parameters can be determined experimentally, which is the task of identification. The book is divided into seven chapters dealing with the following topics: 1. Nonlinear dynamic process models 2. Test signals for identification 3. Parameter estimation methods 4. Nonlinearity test methods 5. Structure identification 6. Model validity tests 7. Case studies on identification of real processes Chapter I summarizes the different model descriptions of nonlinear dynamical systems.
Author: Oliver Nelles
Publisher: Springer Science & Business Media
ISBN: 3662043238
Category : Technology & Engineering
Languages : en
Pages : 786
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Book Description
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.
Author: Robert Haber
Publisher: Springer Science & Business Media
ISBN: 9780792358572
Category : Computers
Languages : en
Pages : 428
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Book Description
This is the second part of a two-volume handbook presenting a comprehensive overview of nonlinear dynamic system identification. The books include many aspects of nonlinear processes such as modelling, parameter estimation, structure search, nonlinearity and model validity tests.
Author: Stephen A. Billings
Publisher: John Wiley & Sons
ISBN: 1118535553
Category : Technology & Engineering
Languages : en
Pages : 576
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Book Description
Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains describes a comprehensive framework for the identification and analysis of nonlinear dynamic systems in the time, frequency, and spatio-temporal domains. This book is written with an emphasis on making the algorithms accessible so that they can be applied and used in practice. Includes coverage of: The NARMAX (nonlinear autoregressive moving average with exogenous inputs) model The orthogonal least squares algorithm that allows models to be built term by term where the error reduction ratio reveals the percentage contribution of each model term Statistical and qualitative model validation methods that can be applied to any model class Generalised frequency response functions which provide significant insight into nonlinear behaviours A completely new class of filters that can move, split, spread, and focus energy The response spectrum map and the study of sub harmonic and severely nonlinear systems Algorithms that can track rapid time variation in both linear and nonlinear systems The important class of spatio-temporal systems that evolve over both space and time Many case study examples from modelling space weather, through identification of a model of the visual processing system of fruit flies, to tracking causality in EEG data are all included to demonstrate how easily the methods can be applied in practice and to show the insight that the algorithms reveal even for complex systems NARMAX algorithms provide a fundamentally different approach to nonlinear system identification and signal processing for nonlinear systems. NARMAX methods provide models that are transparent, which can easily be analysed, and which can be used to solve real problems. This book is intended for graduates, postgraduates and researchers in the sciences and engineering, and also for users from other fields who have collected data and who wish to identify models to help to understand the dynamics of their systems.
Author: Oliver Nelles
Publisher: Springer Science & Business Media
ISBN: 9783540673699
Category : Computers
Languages : en
Pages : 814
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Book Description
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.
Author: Julius S. Bendat
Publisher: Wiley-Interscience
ISBN:
Category : Mathematics
Languages : en
Pages : 304
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Book Description
Describes procedures to identify and analyze the properties of many types of nonlinear systems from random data measured at the input and output points of physical systems. Improvements are offered in applying older techniques, and problems that traditionally have been difficult to analyze are solved by new, simpler procedures. Formulas are stated for optimum nonlinear system identification in both general models consisting of parallel, linear bilinear and trilinear systems, and special models consisting of parallel linear, finite-memory square-law systems and finite-memory cubic systems. New results, obtained here, show when and how to replace complicated single input/output nonlinear models with simpler alternative multiple input/single output linear models. New error analysis formulas are presented to design experiments and to evaluate estimates obtained from measured data. Includes many illustrative examples.
Author: David T. Westwick
Publisher: John Wiley & Sons
ISBN: 9780471274568
Category : Technology & Engineering
Languages : en
Pages : 284
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Book Description
Significant advances have been made in the field since the previous classic texts were written. This text brings the available knowledge up to date. * Enables the reader to use a wide variety of nonlinear system identification techniques. * Offers a thorough treatment of the underlying theory. * Provides a MATLAB toolbox containing implementation of the latest identification methods together with an extensive set of problems using realistic data sets.
Author: Alexandre Mauroy
Publisher: Springer Nature
ISBN: 3030357139
Category : Technology & Engineering
Languages : en
Pages : 568
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Book Description
This book provides a broad overview of state-of-the-art research at the intersection of the Koopman operator theory and control theory. It also reviews novel theoretical results obtained and efficient numerical methods developed within the framework of Koopman operator theory. The contributions discuss the latest findings and techniques in several areas of control theory, including model predictive control, optimal control, observer design, systems identification and structural analysis of controlled systems, addressing both theoretical and numerical aspects and presenting open research directions, as well as detailed numerical schemes and data-driven methods. Each contribution addresses a specific problem. After a brief introduction of the Koopman operator framework, including basic notions and definitions, the book explores numerical methods, such as the dynamic mode decomposition (DMD) algorithm and Arnoldi-based methods, which are used to represent the operator in a finite-dimensional basis and to compute its spectral properties from data. The main body of the book is divided into three parts: theoretical results and numerical techniques for observer design, synthesis analysis, stability analysis, parameter estimation, and identification; data-driven techniques based on DMD, which extract the spectral properties of the Koopman operator from data for the structural analysis of controlled systems; and Koopman operator techniques with specific applications in systems and control, which range from heat transfer analysis to robot control. A useful reference resource on the Koopman operator theory for control theorists and practitioners, the book is also of interest to graduate students, researchers, and engineers looking for an introduction to a novel and comprehensive approach to systems and control, from pure theory to data-driven methods.
Author: Oliver Nelles
Publisher: Springer Nature
ISBN: 3030474399
Category : Science
Languages : en
Pages : 1235
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Book Description
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.
