Neural Network Modeling and Identification of Dynamical Systems

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  • Author : Yury 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 Yury 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 : Yury 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

ISIE 96

ISIE 96
  • Author : IEEE Industrial Electronics Society,Politechnika Warszawska,IEEE Power Electronics Society
  • Publisher : Unknown
  • Release : 22 June 1996
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Identification of Dynamic Systems

Identification of Dynamic Systems
  • Author : Rolf Isermann,Marco Münchhof
  • Publisher : Springer
  • Release : 23 November 2014
GET THIS BOOK Identification of Dynamic Systems

Precise dynamic models of processes are required for many applications, ranging from control engineering to the natural sciences and economics. Frequently, such precise models cannot be derived using theoretical considerations alone. Therefore, they must be determined experimentally. This book treats the determination of dynamic models based on measurements taken at the process, which is known as system identification or process identification. Both offline and online methods are presented, i.e. methods that post-process the measured data as well as methods

System Identification

System Identification
  • Author : Lennart Ljung
  • Publisher : Pearson Education
  • Release : 29 December 1998
GET THIS BOOK System Identification

The field's leading text, now completely updated. Modeling dynamical systems — theory, methodology, and applications. Lennart Ljung's System Identification: Theory for the User is a complete, coherent description of the theory, methodology, and practice of System Identification. This completely revised Second Edition introduces subspace methods, methods that utilize frequency domain data, and general non-linear black box methods, including neural networks and neuro-fuzzy modeling. The book contains many new computer-based examples designed for Ljung's market-leading software, System Identification Toolbox for MATLAB. Ljung

Identification of Nonlinear Systems Using Neural Networks and Polynomial Models

Identification of Nonlinear Systems Using Neural Networks and Polynomial Models
  • Author : Andrzej Janczak
  • Publisher : Springer Science & Business Media
  • Release : 18 November 2004
GET THIS BOOK Identification of Nonlinear Systems Using Neural Networks and Polynomial Models

This monograph systematically presents the existing identification methods of nonlinear systems using the block-oriented approach It surveys various known approaches to the identification of Wiener and Hammerstein systems which are applicable to both neural network and polynomial models. The book gives a comparative study of their gradient approximation accuracy, computational complexity, and convergence rates and furthermore presents some new and original methods concerning the model parameter adjusting with gradient-based techniques. "Identification of Nonlinear Systems Using Neural Networks and Polynomal Models"

Proceedings of the IECON 97

Proceedings of the IECON  97
  • Author : Anonim
  • Publisher : Institute of Electrical & Electronics Engineers(IEEE)
  • Release : 22 June 1997
GET THIS BOOK Proceedings of the IECON 97

With the dynamic global environment, rapid technology changes, the need for updated management skills will be of paramount importance. This conference focuses on applications of electronics in industry, especially in the areas of control and instrumentation.

Proceedings

Proceedings
  • Author : International Workshop on Neural Networks for Identification, Control, Robotics, and Signal/Image Processing,IEEE Computer Society
  • Publisher : IEEE Computer Society
  • Release : 22 June 1996
GET THIS BOOK Proceedings

Annotation A collection of 55 papers presented at this gathering. A sampling of topics: selecting inputs and measuring nonlinearity in system identification, adaptive inverse control based on linear and nonlinear adaptive filtering, system identification using modular neural network with improved learning, asymptotic probability density of the generalization error, neuro-ASIC for low cost supervision of water pollution, high speed vision-based quality grading of oranges, new developments and applications of self-organizing maps, and new geometrical approach for blind separation of sources mapped to