Neural Network Modeling and Identification of Dynamical Systems

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  • Author : Yuri Tiumentsev
  • Publisher : Academic Press
  • Pages : 332 pages
  • ISBN : 0128154306
  • Rating : /5 from reviews
CLICK HERE TO GET THIS BOOK >>>Neural Network Modeling and Identification of Dynamical Systems

Download or Read online Neural Network Modeling and Identification of Dynamical Systems full in PDF, ePub and kindle. this book written by Yuri Tiumentsev and published by Academic Press which was released on 17 May 2019 with total page 332 pages. We cannot guarantee that Neural Network Modeling and Identification of Dynamical Systems book is available in the library, click Get Book button and read full online book in your kindle, tablet, IPAD, PC or mobile whenever and wherever You Like. 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

Neural Network Modeling and Identification of Dynamical Systems

Neural Network Modeling and Identification of Dynamical Systems
  • Author : Yuri Tiumentsev,Mikhail Egorchev
  • Publisher : Academic Press
  • Release : 17 May 2019
GET THIS BOOK Neural Network Modeling and Identification of Dynamical Systems

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

Neural Networks in Robotics

Neural Networks in Robotics
  • Author : George A. Bekey,Kenneth Y. Goldberg
  • Publisher : Springer Science & Business Media
  • Release : 06 December 2012
GET THIS BOOK Neural Networks in Robotics

Neural Networks in Robotics is the first book to present an integrated view of both the application of artificial neural networks to robot control and the neuromuscular models from which robots were created. The behavior of biological systems provides both the inspiration and the challenge for robotics. The goal is to build robots which can emulate the ability of living organisms to integrate perceptual inputs smoothly with motor responses, even in the presence of novel stimuli and changes in the

Neural Networks Modeling and Control

Neural Networks Modeling and Control
  • Author : Jorge D. Rios,Alma Y. Alanis,Nancy Arana-Daniel,Carlos Lopez-Franco
  • Publisher : Academic Press
  • Release : 15 January 2020
GET THIS BOOK Neural Networks Modeling and Control

Neural Networks Modelling and Control: Applications for Unknown Nonlinear Delayed Systems in Discrete Time focuses on modeling and control of discrete-time unknown nonlinear delayed systems under uncertainties based on Artificial Neural Networks. First, a Recurrent High Order Neural Network (RHONN) is used to identify discrete-time unknown nonlinear delayed systems under uncertainties, then a RHONN is used to design neural observers for the same class of systems. Therefore, both neural models are used to synthesize controllers for trajectory tracking based on

Advances in Logic Based Intelligent Systems

Advances in Logic Based Intelligent Systems
  • Author : Kazumi Nakamatsu,Jair Minoro Abe
  • Publisher : IOS Press
  • Release : 16 May 2022
GET THIS BOOK Advances in Logic Based Intelligent Systems

LAPTEC2005 promoted the discussion and interaction between researchers and practitioners focused on both theoretical and practical disciplines concerning logics applied to technology, with diverse backgrounds including all kinds of intelligent systems having classical or non-classical logics as underlying common matters. It was the first time for LAPTEC to be held in a different country than Brazil since its birth in 2000, and this has made the congress more international. This book is dedicated to Emeritus Professor Atsuyuki Suzuki in commemoration of

Advances in Neural Computation Machine Learning and Cognitive Research III

Advances in Neural Computation  Machine Learning  and Cognitive Research III
  • Author : Boris Kryzhanovsky,Witali Dunin-Barkowski,Vladimir Redko,Yury Tiumentsev
  • Publisher : Springer Nature
  • Release : 03 September 2019
GET THIS BOOK Advances in Neural Computation Machine Learning and Cognitive Research III

This book describes new theories and applications of artificial neural networks, with a special focus on answering questions in neuroscience, biology and biophysics and cognitive research. It covers a wide range of methods and technologies, including deep neural networks, large scale neural models, brain computer interface, signal processing methods, as well as models of perception, studies on emotion recognition, self-organization and many more. The book includes both selected and invited papers presented at the XXI International Conference on Neuroinformatics, held

High Dimensional Neurocomputing

High Dimensional Neurocomputing
  • Author : Bipin Kumar Tripathi
  • Publisher : Springer
  • Release : 05 November 2014
GET THIS BOOK High Dimensional Neurocomputing

The book presents a coherent understanding of computational intelligence from the perspective of what is known as "intelligent computing" with high-dimensional parameters. It critically discusses the central issue of high-dimensional neurocomputing, such as quantitative representation of signals, extending the dimensionality of neuron, supervised and unsupervised learning and design of higher order neurons. The strong point of the book is its clarity and ability of the underlying theory to unify our understanding of high-dimensional computing where conventional methods fail. The plenty

Artificial Higher Order Neural Networks for Computer Science and Engineering Trends for Emerging Applications

Artificial Higher Order Neural Networks for Computer Science and Engineering  Trends for Emerging Applications
  • Author : Zhang, Ming
  • Publisher : IGI Global
  • Release : 28 February 2010
GET THIS BOOK Artificial Higher Order Neural Networks for Computer Science and Engineering Trends for Emerging Applications

"This book introduces and explains Higher Order Neural Networks (HONNs) to people working in the fields of computer science and computer engineering, and how to use HONNS in these areas"--Provided by publisher.

Emerging Capabilities and Applications of Artificial Higher Order Neural Networks

Emerging Capabilities and Applications of Artificial Higher Order Neural Networks
  • Author : Zhang, Ming
  • Publisher : IGI Global
  • Release : 05 February 2021
GET THIS BOOK Emerging Capabilities and Applications of Artificial Higher Order Neural Networks

Artificial neural network research is one of the new directions for new generation computers. Current research suggests that open box artificial higher order neural networks (HONNs) play an important role in this new direction. HONNs will challenge traditional artificial neural network products and change the research methodology that people are currently using in control and recognition areas for the control signal generating, pattern recognition, nonlinear recognition, classification, and prediction. Since HONNs are open box models, they can be easily accepted

Neural Networks and Soft Computing

Neural Networks and Soft Computing
  • Author : Leszek Rutkowski,Janusz Kacprzyk
  • Publisher : Springer Science & Business Media
  • Release : 12 February 2003
GET THIS BOOK Neural Networks and Soft Computing

This volume presents new trends and developments in soft computing techniques. Topics include: neural networks, fuzzy systems, evolutionary computation, knowledge discovery, rough sets, and hybrid methods. It also covers various applications of soft computing techniques in economics, mechanics, medicine, automatics and image processing. The book contains contributions from internationally recognized scientists, such as Zadeh, Bubnicki, Pawlak, Amari, Batyrshin, Hirota, Koczy, Kosinski, Novák, S.-Y. Lee, Pedrycz, Raudys, Setiono, Sincak, Strumillo, Takagi, Usui, Wilamowski and Zurada. An excellent overview of

Strategies for Feedback Linearisation

Strategies for Feedback Linearisation
  • Author : Freddy Rafael Garces,Victor Manuel Becerra,Chandrasekhar Kambhampati,Kevin Warwick
  • Publisher : Springer Science & Business Media
  • Release : 06 December 2012
GET THIS BOOK Strategies for Feedback Linearisation

Using relevant mathematical proofs and case studies illustrating design and application issues, this book demonstrates this powerful technique in the light of research on neural networks, which allow the identification of nonlinear models without the complicated and costly development of models based on physical laws.

Signal Processing for Mobile Communications Handbook

Signal Processing for Mobile Communications Handbook
  • Author : Mohamed Ibnkahla
  • Publisher : CRC Press
  • Release : 16 August 2004
GET THIS BOOK Signal Processing for Mobile Communications Handbook

In recent years, a wealth of research has emerged addressing various aspects of mobile communications signal processing. New applications and services are continually arising, and future mobile communications offer new opportunities and exciting challenges for signal processing. The Signal Processing for Mobile Communications Handbook provi

Modelling Simulation and Control of Non linear Dynamical Systems

Modelling  Simulation and Control of Non linear Dynamical Systems
  • Author : Patricia Melin,Oscar Castillo
  • Publisher : CRC Press
  • Release : 25 October 2001
GET THIS BOOK Modelling Simulation and Control of Non linear Dynamical Systems

These authors use soft computing techniques and fractal theory in this new approach to mathematical modeling, simulation and control of complexion-linear dynamical systems. First, a new fuzzy-fractal approach to automated mathematical modeling of non-linear dynamical systems is presented. It is illustrated with examples on the PROLOG programming la

Computer Aided Systems Theory EUROCAST 95

Computer Aided Systems Theory   EUROCAST  95
  • Author : International Workshop on Computer Aided Systems Theory 1995 innsbruc,Franz Pichler,Roberto Moreno-Diaz
  • Publisher : Springer Science & Business Media
  • Release : 24 January 1996
GET THIS BOOK Computer Aided Systems Theory EUROCAST 95

This book presents a collection of revised refereed papers selected from the contributions to the Fifth International Workshop on Computer Aided Systems Theory, EUROCAST '95, held in Innsbruck, Austria in May 1995. The 42 full papers contained have been contributed by CAST theoreticians, tool-makers, designers, and appliers and reflect the full spectrum of activities in the area. The papers are organized in sections on systems theory, design environments, complex systems design, and specific applications.

Soft Computing in Communications

Soft Computing in Communications
  • Author : Lipo Wang
  • Publisher : Springer
  • Release : 05 June 2013
GET THIS BOOK Soft Computing in Communications

Soft computing, as opposed to conventional "hard" computing, tolerates imprecision and uncertainty, in a way very much similar to the human mind. Soft computing techniques include neural networks, evolutionary computation, fuzzy logic, and chaos. The recent years have witnessed tremendous success of these powerful methods in virtually all areas of science and technology, as evidenced by the large numbers of research results published in a variety of journals, conferences, as weil as many excellent books in this book series on

Issues in Artificial Intelligence Robotics and Machine Learning 2011 Edition

Issues in Artificial Intelligence  Robotics and Machine Learning  2011 Edition
  • Author : Anonim
  • Publisher : ScholarlyEditions
  • Release : 09 January 2012
GET THIS BOOK Issues in Artificial Intelligence Robotics and Machine Learning 2011 Edition

Issues in Artificial Intelligence, Robotics and Machine Learning: 2011 Edition is a ScholarlyEditions™ eBook that delivers timely, authoritative, and comprehensive information about Artificial Intelligence, Robotics and Machine Learning. The editors have built Issues in Artificial Intelligence, Robotics and Machine Learning: 2011 Edition on the vast information databases of ScholarlyNews.™ You can expect the information about Artificial Intelligence, Robotics and Machine Learning in this eBook to be deeper than what you can access anywhere else, as well as consistently reliable, authoritative, informed, and