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 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

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  • Author : Yuri Tiumentsev
  • Publisher : Academic Press
  • Pages : 332 pages
  • ISBN : 0128154306
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKNeural Network Modeling and Identification of Dynamical Systems

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 BOOKNeural 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

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 BOOKAdvances 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

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 BOOKNeural 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

Industrial Applications of Power Electronics

Industrial Applications of Power Electronics
  • Author : Eduardo M. G. Rodrigues,Edris Pouresmaeil,Radu Godina
  • Publisher : MDPI
  • Release : 01 December 2020
GET THIS BOOKIndustrial Applications of Power Electronics

In recent years, power electronics have been intensely contributing to the development and evolution of new structures for the processing of energy. They can be used in a wide range of applications ranging from power systems and electrical machines to electric vehicles and robot arm drives. In conjunction with the evolution of microprocessors and advanced control theories, power electronics are playing an increasingly essential role in our society. Thus, in order to cope with the obstacles lying ahead, this book

Recent Advances of Neural Network Models and Applications

Recent Advances of Neural Network Models and Applications
  • Author : Simone Bassis,Anna Esposito,Francesco Carlo Morabito
  • Publisher : Springer Science & Business Media
  • Release : 19 December 2013
GET THIS BOOKRecent Advances of Neural Network Models and Applications

This volume collects a selection of contributions which has been presented at the 23rd Italian Workshop on Neural Networks, the yearly meeting of the Italian Society for Neural Networks (SIREN). The conference was held in Vietri sul Mare, Salerno, Italy during May 23-24, 2013. The annual meeting of SIREN is sponsored by International Neural Network Society (INNS), European Neural Network Society (ENNS) and IEEE Computational Intelligence Society (CIS). The book – as well as the workshop- is organized in two main components,

Nonlinear System Identification

Nonlinear System Identification
  • Author : Oliver Nelles
  • Publisher : Springer Science & Business Media
  • Release : 09 March 2013
GET THIS BOOKNonlinear System Identification

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.

Multi-Resolution Methods for Modeling and Control of Dynamical Systems

Multi-Resolution Methods for Modeling and Control of Dynamical Systems
  • Author : Puneet Singla,John L. Junkins
  • Publisher : CRC Press
  • Release : 01 August 2008
GET THIS BOOKMulti-Resolution Methods for Modeling and Control of Dynamical Systems

Unifying the most important methodology in this field, Multi-Resolution Methods for Modeling and Control of Dynamical Systems explores existing approximation methods as well as develops new ones for the approximate solution of large-scale dynamical system problems. It brings together a wide set of material from classical orthogonal function approximation, neural network input-output approximation, finite element methods for distributed parameter systems, and various approximation methods employed in adaptive control and learning theory. With sufficient rigor and generality, the book promotes a

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 BOOKComputer 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.

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 BOOKNeural 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

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 BOOKEmerging 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

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 BOOKArtificial 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.

Artificial Neural Networks for Engineering Applications

Artificial Neural Networks for Engineering Applications
  • Author : Alma Y. Alanis,Nancy Arana-Daniel,Carlos Lopez-Franco
  • Publisher : Academic Press
  • Release : 15 March 2019
GET THIS BOOKArtificial Neural Networks for Engineering Applications

Artificial Neural Networks for Engineering Applications presents current trends for the solution of complex engineering problems that cannot be solved through conventional methods. The proposed methodologies can be applied to modeling, pattern recognition, classification, forecasting, estimation, and more. Readers will find different methodologies to solve various problems, including complex nonlinear systems, cellular computational networks, waste water treatment, attack detection on cyber-physical systems, control of UAVs, biomechanical and biomedical systems, time series forecasting, biofuels, and more. Besides the real-time implementations, the

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 BOOKIssues 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