Neural Network Modeling And Identification Of Dynamical Systems
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Neural Network Modeling and Identification of Dynamical Systems
Author | : Yuri Tiumentsev,Mikhail Egorchev |
Publsiher | : Academic Press |
Total Pages | : 332 |
Release | : 2019-05-17 |
Genre | : Science |
ISBN | : 9780128154304 |
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Neural Network Modeling and Identification of Dynamical Systems presents a new approach on how to obtain the adaptive neural network models for complex systems that are typically found in real-world applications. The book introduces the theoretical knowledge available for the modeled system into the purely empirical black box model, thereby converting the model to the gray box category. This approach significantly reduces the dimension of the resulting model and the required size of the training set. This book offers solutions for identifying controlled dynamical systems, as well as identifying characteristics of such systems, in particular, the aerodynamic characteristics of aircraft. Covers both types of dynamic neural networks (black box and gray box) including their structure, synthesis and training Offers application examples of dynamic neural network technologies, primarily related to aircraft Provides an overview of recent achievements and future needs in this area
Nonlinear System Identification
Author | : Oliver Nelles |
Publsiher | : Springer Science & Business Media |
Total Pages | : 786 |
Release | : 2013-03-09 |
Genre | : Technology & Engineering |
ISBN | : 9783662043233 |
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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.
Nonlinear System Identification
Author | : Oliver Nelles |
Publsiher | : Springer Nature |
Total Pages | : 1235 |
Release | : 2020-09-09 |
Genre | : Science |
ISBN | : 9783030474393 |
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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.
Neural Network Systems Techniques and Applications
Author | : Anonim |
Publsiher | : Academic Press |
Total Pages | : 438 |
Release | : 1998-02-09 |
Genre | : Computers |
ISBN | : 0080553907 |
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The book emphasizes neural network structures for achieving practical and effective systems, and provides many examples. Practitioners, researchers, and students in industrial, manufacturing, electrical, mechanical,and production engineering will find this volume a unique and comprehensive reference source for diverse application methodologies. Control and Dynamic Systems covers the important topics of highly effective Orthogonal Activation Function Based Neural Network System Architecture, multi-layer recurrent neural networks for synthesizing and implementing real-time linear control,adaptive control of unknown nonlinear dynamical systems, Optimal Tracking Neural Controller techniques, a consideration of unified approximation theory and applications, techniques for the determination of multi-variable nonlinear model structures for dynamic systems with a detailed treatment of relevant system model input determination, High Order Neural Networks and Recurrent High Order Neural Networks, High Order Moment Neural Array Systems, Online Learning Neural Network controllers, and Radial Bias Function techniques. Coverage includes: Orthogonal Activation Function Based Neural Network System Architecture (OAFNN) Multilayer recurrent neural networks for synthesizing and implementing real-time linear control Adaptive control of unknown nonlinear dynamical systems Optimal Tracking Neural Controller techniques Consideration of unified approximation theory and applications Techniques for determining multivariable nonlinear model structures for dynamic systems, with a detailed treatment of relevant system model input determination
Data Driven Science and Engineering
Author | : Steven L. Brunton,J. Nathan Kutz |
Publsiher | : Cambridge University Press |
Total Pages | : 615 |
Release | : 2022-05-05 |
Genre | : Computers |
ISBN | : 9781009098489 |
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A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
Nonlinear Dynamical Systems
Author | : Irwin W. Sandberg,James T. Lo,Craig L. Fancourt,José C. Principe,Shigeru Katagiri,Simon Haykin |
Publsiher | : John Wiley & Sons |
Total Pages | : 316 |
Release | : 2001-02-21 |
Genre | : Technology & Engineering |
ISBN | : 0471349119 |
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Sechs erfahrene Autoren beschreiben in diesem Band ein Spezialgebiet der neuronalen Netze mit Anwendungen in der Signalsteuerung, Signalverarbeitung und Zeitreihenanalyse. Ein zeitgemäßer Beitrag zur Behandlung nichtlinear-dynamischer Systeme!
Neural Networks for Modelling and Control of Dynamic Systems
Author | : M. Norgaard |
Publsiher | : Unknown |
Total Pages | : 246 |
Release | : 2003 |
Genre | : Electronic Book |
ISBN | : OCLC:876537456 |
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Control and Dynamic Systems
Author | : Cornelius T. Leondes |
Publsiher | : Unknown |
Total Pages | : 438 |
Release | : 1998 |
Genre | : Computers |
ISBN | : 9780124438675 |
Download Control and Dynamic Systems Book in PDF, Epub and Kindle
The book emphasizes neural network structures for achieving practical and effective systems, and provides many examples. Practitioners, researchers, and students in industrial, manufacturing, electrical, mechanical,and production engineering will find this volume a unique and comprehensive reference source for diverse application methodologies. Control and Dynamic Systems covers the important topics of highly effective Orthogonal Activation Function Based Neural Network System Architecture, multi-layer recurrent neural networks for synthesizing and implementing real-time linear control,adaptive control of unknown nonlinear dynamical systems, Optimal Tracking Neural Controller techniques, a consideration of unified approximation theory and applications, techniques for the determination of multi-variable nonlinear model structures for dynamic systems with a detailed treatment of relevant system model input determination, High Order Neural Networks and Recurrent High Order Neural Networks, High Order Moment Neural Array Systems, Online Learning Neural Network controllers, and Radial Bias Function techniques. Key Features Coverage includes: * Orthogonal Activation Function Based Neural Network System Architecture (OAFNN) * Multilayer recurrent neural networks for synthesizing and implementing real-time linear control * Adaptive control of unknown nonlinear dynamical systems * Optimal Tracking Neural Controller techniques * Consideration of unified approximation theory and applications * Techniques for determining multivariable nonlinear model structures for dynamic systems, with a detailed treatment of relevant system model input determination