Discrete Time Neural Observers

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  • Publisher : Academic Press
  • Release : 06 February 2017
  • ISBN : 9780128105443
  • Page : 150 pages
  • Rating : 4.5/5 from 103 voters

Discrete Time Neural Observers Book PDF summary

Discrete-Time Neural Observers: Analysis and Applications presents recent advances in the theory of neural state estimation for discrete-time unknown nonlinear systems with multiple inputs and outputs. The book includes rigorous mathematical analyses, based on the Lyapunov approach, that guarantee their properties. In addition, for each chapter, simulation results are included to verify the successful performance of the corresponding proposed schemes. In order to complete the treatment of these schemes, the authors also present simulation and experimental results related to their application in meaningful areas, such as electric three phase induction motors and anaerobic process, which show the applicability of such designs. The proposed schemes can be employed for different applications beyond those presented. The book presents solutions for the state estimation problem of unknown nonlinear systems based on two schemes. For the first one, a full state estimation problem is considered; the second one considers the reduced order case with, and without, the presence of unknown delays. Both schemes are developed in discrete-time using recurrent high order neural networks in order to design the neural observers, and the online training of the respective neural networks is performed by Kalman Filtering. Presents online learning for Recurrent High Order Neural Networks (RHONN) using the Extended Kalman Filter (EKF) algorithm Contains full and reduced order neural observers for discrete-time unknown nonlinear systems, with and without delays Includes rigorous analyses of the proposed schemes, including the nonlinear system, the respective observer, and the Kalman filter learning Covers real-time implementation and simulation results for all the proposed schemes to meaningful applications

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Discrete-Time Neural Observers

Discrete-Time Neural Observers
  • Author : Alma Y. Alanis,Edgar N Sanchez
  • Publisher : Academic Press
  • Release Date : 2017-02-06
  • ISBN : 9780128105443
DOWNLOAD BOOKDiscrete-Time Neural Observers

Discrete-Time Neural Observers: Analysis and Applications presents recent advances in the theory of neural state estimation for discrete-time unknown nonlinear systems with multiple inputs and outputs. The book includes rigorous mathematical analyses, based on the Lyapunov approach, that guarantee their properties. In addition, for each chapter, simulation results are included to verify the successful performance of the corresponding proposed schemes. In order to complete the treatment of these schemes, the authors also present simulation and experimental results related to their

Discrete-Time High Order Neural Control

Discrete-Time High Order Neural Control
  • Author : Edgar N. Sanchez,Alma Y. Alanís,Alexander G. Loukianov
  • Publisher : Springer Science & Business Media
  • Release Date : 2008-04-29
  • ISBN : 9783540782889
DOWNLOAD BOOKDiscrete-Time High Order Neural Control

Neural networks have become a well-established methodology as exempli?ed by their applications to identi?cation and control of general nonlinear and complex systems; the use of high order neural networks for modeling and learning has recently increased. Usingneuralnetworks,controlalgorithmscanbedevelopedtoberobustto uncertainties and modeling errors. The most used NN structures are Feedf- ward networks and Recurrent networks. The latter type o?ers a better suited tool to model and control of nonlinear systems. There exist di?erent training algorithms for neural

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 Date : 2020-01-15
  • ISBN : 9780128170793
DOWNLOAD 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

Advances in Computational Intelligence

Advances in Computational Intelligence
  • Author : Wen Yu,Edgar N. Sanchez
  • Publisher : Springer Science & Business Media
  • Release Date : 2009-08-18
  • ISBN : 9783642031564
DOWNLOAD BOOKAdvances in Computational Intelligence

This book constitutes the proceedings of the second International Workshop on Advanced Computational Intelligence (IWACI 2009), with a sequel of IWACI 2008 successfully held in Macao, China. IWACI 2009 provided a high-level international forum for scientists, engineers, and educators to present state-of-the-art research in computational intelligence and related fields. Over the past decades, computational intelligence community has witnessed t- mendous efforts and developments in all aspects of theoretical foundations, archit- tures and network organizations, modelling and simulation, empirical study, as well as a

Foundations of Fuzzy Logic and Soft Computing

Foundations of Fuzzy Logic and Soft Computing
  • Author : Patricia Melin,Oscar Castillo,Luis T. Aguilar,Witold Pedrycz
  • Publisher : Springer Science & Business Media
  • Release Date : 2007-07-02
  • ISBN : 9783540729501
DOWNLOAD BOOKFoundations of Fuzzy Logic and Soft Computing

This book comprises a selection of papers from IFSA 2007 on new methods and theories that contribute to the foundations of fuzzy logic and soft computing. Coverage includes the application of fuzzy logic and soft computing in flexible querying, philosophical and human-scientific aspects of soft computing, search engine and information processing and retrieval, as well as intelligent agents and knowledge ant colony.

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 Date : 2019-02-07
  • ISBN : 9780128182482
DOWNLOAD 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

Robust Discrete-Time Flight Control of UAV with External Disturbances

Robust Discrete-Time Flight Control of UAV with External Disturbances
  • Author : Shuyi Shao,Mou Chen,Peng Shi
  • Publisher : Springer Nature
  • Release Date : 2020-09-26
  • ISBN : 9783030579579
DOWNLOAD BOOKRobust Discrete-Time Flight Control of UAV with External Disturbances

This book studies selected discrete-time flight control schemes for fixed-wing unmanned aerial vehicle (UAV) systems in the presence of system uncertainties, external disturbances and input saturation. The main contributions of this book for UAV systems are as follows: (i) the proposed integer-order discrete-time control schemes are based on the designed discrete-time disturbance observers (DTDOs) and the neural network (NN); and (ii) the fractional-order discrete-time control schemes are developed by using the fractional-order calculus theory, the NN and the DTDOs. The

Discrete-Time High Order Neural Control

Discrete-Time High Order Neural Control
  • Author : Edgar N. Sanchez,Alma Y. Alanís,Alexander G. Loukianov
  • Publisher : Springer
  • Release Date : 2008-06-24
  • ISBN : 9783540782896
DOWNLOAD BOOKDiscrete-Time High Order Neural Control

Neural networks have become a well-established methodology as exempli?ed by their applications to identi?cation and control of general nonlinear and complex systems; the use of high order neural networks for modeling and learning has recently increased. Usingneuralnetworks,controlalgorithmscanbedevelopedtoberobustto uncertainties and modeling errors. The most used NN structures are Feedf- ward networks and Recurrent networks. The latter type o?ers a better suited tool to model and control of nonlinear systems. There exist di?erent training algorithms for neural