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 : Yury 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 : Yury 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

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 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

Soft Computing

Soft Computing
  • Author : L. Fortuna,G.. Rizzotto,M. Lavorgna,G. Nunnari,M.G. Xibilia,Riccardo Caponetto
  • Publisher : Springer Science & Business Media
  • Release : 06 December 2012
GET THIS BOOKSoft Computing

The book presents a clear understanding of a new type of computation system, the Cellular Neural Network (CNN), which has been successfully applied to the solution of many heavy computation problems, mainly in the fields of image processing and complex partial differential equations. The text describes how CNN will improve the soft-computation toolbox, and examines the many applications of soft computing to complex systems.

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,

Identification of Dynamic Systems

Identification of Dynamic Systems
  • Author : Rolf Isermann,Marco Münchhof
  • Publisher : Springer Science & Business Media
  • Release : 22 November 2010
GET THIS BOOKIdentification 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

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

Neural Systems for Control

Neural Systems for Control
  • Author : Omid Omidvar,David L. Elliott
  • Publisher : Elsevier
  • Release : 24 February 1997
GET THIS BOOKNeural Systems for Control

Control problems offer an industrially important application and a guide to understanding control systems for those working in Neural Networks. Neural Systems for Control represents the most up-to-date developments in the rapidly growing aplication area of neural networks and focuses on research in natural and artifical neural systems directly applicable to control or making use of modern control theory. The book covers such important new developments in control systems such as intelligent sensors in semiconductor wafer manufacturing; the relation between

ISIE'96

ISIE'96
  • Author : IEEE Industrial Electronics Society,Politechnika Warszawska,IEEE Power Electronics Society
  • Publisher : Unknown Publisher
  • Release : 21 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 BOOKIdentification 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

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

Proceedings of the IECON '97

Proceedings of the IECON '97
  • Author : Anonim
  • Publisher : Institute of Electrical & Electronics Engineers(IEEE)
  • Release : 21 June 1997
GET THIS BOOKProceedings 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.

System Identification

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