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Discrete-Time Neural Observers

Discrete-Time Neural Observers
Analysis and Applications

by Alma Y. Alanis,Edgar N Sanchez

  • Publisher : Academic Press
  • Release : 2017-02-06
  • Pages : 150
  • ISBN : 0128105445
  • Language : En, Es, Fr & De
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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

Discrete-Time High Order Neural Control

Discrete-Time High Order Neural Control
Trained with Kalman Filtering

by Edgar N. Sanchez,Alma Y. Alanís,Alexander G. Loukianov

  • Publisher : Springer
  • Release : 2008-06-24
  • Pages : 110
  • ISBN : 3540782893
  • Language : En, Es, Fr & De
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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 networks, which, h- ever, normally encounter some technical problems such as local minima, slow learning, and high sensitivity to initial conditions, among others. As a viable alternative, new training algorithms, for example, those based on Kalman ?ltering, have been proposed. There already exists publications about trajectory tracking using neural networks; however, most of those works were developed for continuous-time systems. On the other hand, while extensive literature is available for linear discrete-timecontrolsystem,nonlineardiscrete-timecontroldesigntechniques have not been discussed to the same degree. Besides, discrete-time neural networks are better ?tted for real-time implementations.

Discrete-time Neural Network Based State Observer with Neural Network Based Control Formulation for a Class of Systems with Unmatched Uncertainties

Discrete-time Neural Network Based State Observer with Neural Network Based Control Formulation for a Class of Systems with Unmatched Uncertainties
A Book

by Jason Michael Stumfoll

  • Publisher : Unknown Publisher
  • Release : 2015
  • Pages : 104
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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"An observer is a dynamic system that estimates the state variables of another system using noisy measurements, either to estimate unmeasurable states, or to improve the accuracy of the state measurements. The Modified State Observer (MSO) is a technique that uses a standard observer structure modified to include a neural network to estimate system states as well as system uncertainty. It has been used in orbit uncertainty estimation and atmospheric reentry uncertainty estimation problems to correctly estimate unmodeled system dynamics. A form of the MSO has been used to control a nonlinear electrohydraulic system with parameter uncertainty using a simplified linear model. In this paper an extension of the MSO into discrete-time is developed using Lyapunov stability theory. Discrete-time systems are found in all digital hardware implementations, such as that found in a Martian rover, a quadcopter UAV, or digital flight control systems, and have the added benefit of reduced computation time compared to continuous systems. The derived adaptive update law guarantees stability of the error dynamics and boundedness of the neural network weights. To prove the validity of the discrete-time MSO (DMSO) simulation studies are performed using a two wheeled inverted pendulum (TWIP) robot, an unstable nonlinear system with unmatched uncertainties. Using a linear model with parameter uncertainties, the DMSO is shown to correctly estimate the state of the system as well as the system uncertainty, providing state estimates orders of magnitude more accurate, and in periods of time up to 10 times faster than the Discrete Kalman Filter. The DMSO is implemented on an actual TWIP robot to further validate the performance and demonstrate the applicability to discrete-time systems found in many aerospace applications. Additionally, a new form of neural network control is developed to compensate for the unmatched uncertainties that exist in the TWIP system using a state variable as a virtual control input. It is shown that in all cases the neural network based control assists with the controller effectiveness, resulting in the most effective controller, performing on average 53.1% better than LQR control alone"--Abstract, page iii.

Artificial Neural Networks for Engineering Applications

Artificial Neural Networks for Engineering Applications
A Book

by Alma Y. Alanis,Nancy Arana-Daniel,Carlos Lopez-Franco

  • Publisher : Academic Press
  • Release : 2019-03-15
  • Pages : 224
  • ISBN : 0128182474
  • Language : En, Es, Fr & De
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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 book contains all the theory required to use the proposed methodologies for different applications. Presents the current trends for the solution of complex engineering problems that cannot be solved through conventional methods Includes real-life scenarios where a wide range of artificial neural network architectures can be used to solve the problems encountered in engineering Contains all the theory required to use the proposed methodologies for different applications

Neural Networks Modeling and Control

Neural Networks Modeling and Control
Applications for Unknown Nonlinear Delayed Systems in Discrete Time

by Jorge D. Rios,Alma Y. Alanis,Nancy Arana-Daniel,Carlos Lopez-Franco

  • Publisher : Academic Press
  • Release : 2020-01-15
  • Pages : 158
  • ISBN : 0128170794
  • Language : En, Es, Fr & De
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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 two methodologies: sliding mode control and Inverse Optimal Neural Control. As well as considering the different neural control models and complications that are associated with them, this book also analyzes potential applications, prototypes and future trends. Provide in-depth analysis of neural control models and methodologies Presents a comprehensive review of common problems in real-life neural network systems Includes an analysis of potential applications, prototypes and future trends

Neural Network Control of Nonlinear Discrete-Time Systems

Neural Network Control of Nonlinear Discrete-Time Systems
A Book

by Jagannathan Sarangapani

  • Publisher : CRC Press
  • Release : 2018-10-03
  • Pages : 624
  • ISBN : 1420015451
  • Language : En, Es, Fr & De
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Intelligent systems are a hallmark of modern feedback control systems. But as these systems mature, we have come to expect higher levels of performance in speed and accuracy in the face of severe nonlinearities, disturbances, unforeseen dynamics, and unstructured uncertainties. Artificial neural networks offer a combination of adaptability, parallel processing, and learning capabilities that outperform other intelligent control methods in more complex systems. Borrowing from Biology Examining neurocontroller design in discrete-time for the first time, Neural Network Control of Nonlinear Discrete-Time Systems presents powerful modern control techniques based on the parallelism and adaptive capabilities of biological nervous systems. At every step, the author derives rigorous stability proofs and presents simulation examples to demonstrate the concepts. Progressive Development After an introduction to neural networks, dynamical systems, control of nonlinear systems, and feedback linearization, the book builds systematically from actuator nonlinearities and strict feedback in nonlinear systems to nonstrict feedback, system identification, model reference adaptive control, and novel optimal control using the Hamilton-Jacobi-Bellman formulation. The author concludes by developing a framework for implementing intelligent control in actual industrial systems using embedded hardware. Neural Network Control of Nonlinear Discrete-Time Systems fosters an understanding of neural network controllers and explains how to build them using detailed derivations, stability analysis, and computer simulations.

Artificial Higher Order Neural Networks for Modeling and Simulation

Artificial Higher Order Neural Networks for Modeling and Simulation
A Book

by Zhang, Ming

  • Publisher : IGI Global
  • Release : 2012-10-31
  • Pages : 454
  • ISBN : 1466621761
  • Language : En, Es, Fr & De
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"This book introduces Higher Order Neural Networks (HONNs) to computer scientists and computer engineers as an open box neural networks tool when compared to traditional artificial neural networks"--Provided by publisher.

2000 Power Engineering Society Summer Meeting

2000 Power Engineering Society Summer Meeting
Conference Proceedings : 16-20 July 2000, Seattle, Washington USA

by IEEE Power Engineering Society. Summer Meeting

  • Publisher : Unknown Publisher
  • Release : 2000
  • Pages : 2607
  • ISBN : 9780780364219
  • Language : En, Es, Fr & De
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Network and Communication Technology Innovations for Web and IT Advancement

Network and Communication Technology Innovations for Web and IT Advancement
A Book

by Alkhatib, Ghazi I.

  • Publisher : IGI Global
  • Release : 2012-10-31
  • Pages : 355
  • ISBN : 1466621583
  • Language : En, Es, Fr & De
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With the steady stream of new web based information technologies being introduced to organizations, the need for network and communication technologies to provide an easy integration of knowledge and information sharing is essential. Network and Communication Technology Innovations for Web and IT Advancement presents studies on trends, developments, and methods on information technology advancements through network and communication technology. This collection brings together integrated approaches for communication technology and usage for web and IT advancements.

Advanced Discrete-Time Control

Advanced Discrete-Time Control
Designs and Applications

by Khalid Abidi,Jian-Xin Xu

  • Publisher : Springer
  • Release : 2015-03-25
  • Pages : 224
  • ISBN : 981287478X
  • Language : En, Es, Fr & De
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This book covers a wide spectrum of systems such as linear and nonlinear multivariable systems as well as control problems such as disturbance, uncertainty and time-delays. The purpose of this book is to provide researchers and practitioners a manual for the design and application of advanced discrete-time controllers. The book presents six different control approaches depending on the type of system and control problem. The first and second approaches are based on Sliding Mode control (SMC) theory and are intended for linear systems with exogenous disturbances. The third and fourth approaches are based on adaptive control theory and are aimed at linear/nonlinear systems with periodically varying parametric uncertainty or systems with input delay. The fifth approach is based on Iterative learning control (ILC) theory and is aimed at uncertain linear/nonlinear systems with repeatable tasks and the final approach is based on fuzzy logic control (FLC) and is intended for highly uncertain systems with heuristic control knowledge. Detailed numerical examples are provided in each chapter to illustrate the design procedure for each control method. A number of practical control applications are also presented to show the problem solving process and effectiveness with the advanced discrete-time control approaches introduced in this book.

Neural Information Processing

Neural Information Processing
20th International Conference, ICONIP 2013, Daegu, Korea, November 3-7, 2013. Proceedings

by Minho Lee,Akira Hirose,Zeng-Guang Hou,Rhee Man Kil

  • Publisher : Springer
  • Release : 2013-10-29
  • Pages : 646
  • ISBN : 3642420540
  • Language : En, Es, Fr & De
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The three volume set LNCS 8226, LNCS 8227, and LNCS 8228 constitutes the proceedings of the 20th International Conference on Neural Information Processing, ICONIP 2013, held in Daegu, Korea, in November 2013. The 180 full and 75 poster papers presented together with 4 extended abstracts were carefully reviewed and selected from numerous submissions. These papers cover all major topics of theoretical research, empirical study and applications of neural information processing research. The specific topics covered are as follows: cognitive science and artificial intelligence; learning theory, algorithms and architectures; computational neuroscience and brain imaging; vision, speech and signal processing; control, robotics and hardware technologies and novel approaches and applications.

1997 IEEE International Conference on Intelligent Process Systems.

1997 IEEE International Conference on Intelligent Process Systems.
October 28-31, 1997, Central Garden Hotel, Beijing, China

by Anonim

  • Publisher : Unknown Publisher
  • Release : 1997
  • Pages : 941
  • ISBN : 9787800034107
  • Language : En, Es, Fr & De
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1997 IEEE International Conference on Intelligent Processing Systems ; [papers]

1997 IEEE International Conference on Intelligent Processing Systems ; [papers]
October 28-31, 1997, Central Garden Hotel, Beijing, China

by IEEE Industrial Electronics Society

  • Publisher : Unknown Publisher
  • Release : 1997
  • Pages : 1893
  • ISBN : 9787800034107
  • Language : En, Es, Fr & De
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Scientia Iranica

Scientia Iranica
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 2004
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Neural Network Control of Nonlinear Discrete-Time Systems

Neural Network Control of Nonlinear Discrete-Time Systems
A Book

by Jagannathan Sarangapani

  • Publisher : CRC Press
  • Release : 2018-10-03
  • Pages : 624
  • ISBN : 1420015451
  • Language : En, Es, Fr & De
GET BOOK

Intelligent systems are a hallmark of modern feedback control systems. But as these systems mature, we have come to expect higher levels of performance in speed and accuracy in the face of severe nonlinearities, disturbances, unforeseen dynamics, and unstructured uncertainties. Artificial neural networks offer a combination of adaptability, parallel processing, and learning capabilities that outperform other intelligent control methods in more complex systems. Borrowing from Biology Examining neurocontroller design in discrete-time for the first time, Neural Network Control of Nonlinear Discrete-Time Systems presents powerful modern control techniques based on the parallelism and adaptive capabilities of biological nervous systems. At every step, the author derives rigorous stability proofs and presents simulation examples to demonstrate the concepts. Progressive Development After an introduction to neural networks, dynamical systems, control of nonlinear systems, and feedback linearization, the book builds systematically from actuator nonlinearities and strict feedback in nonlinear systems to nonstrict feedback, system identification, model reference adaptive control, and novel optimal control using the Hamilton-Jacobi-Bellman formulation. The author concludes by developing a framework for implementing intelligent control in actual industrial systems using embedded hardware. Neural Network Control of Nonlinear Discrete-Time Systems fosters an understanding of neural network controllers and explains how to build them using detailed derivations, stability analysis, and computer simulations.

Index to IEEE Publications

Index to IEEE Publications
A Book

by Institute of Electrical and Electronics Engineers

  • Publisher : Unknown Publisher
  • Release : 1996
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Joint 9th IFSA World Congress and 20th NAFIPS International Conference

Joint 9th IFSA World Congress and 20th NAFIPS International Conference
Proceedings : July 25-28, 2001, Vancouver, British Columbia, Canada

by International Fuzzy Systems Association

  • Publisher : Unknown Publisher
  • Release : 2001
  • Pages : 3100
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Journal of Dynamic Systems, Measurement, and Control

Journal of Dynamic Systems, Measurement, and Control
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 2001
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Computer & Control Abstracts

Computer & Control Abstracts
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 1996
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Proceedings of the 1999 IEEE International Symposium on Intelligent Control, Intelligent Systems & Semiotics

Proceedings of the 1999 IEEE International Symposium on Intelligent Control, Intelligent Systems & Semiotics
September 15-17, 1999, Cambridge, MA

by Anonim

  • Publisher : Unknown Publisher
  • Release : 1999
  • Pages : 464
  • ISBN : 9780780356665
  • Language : En, Es, Fr & De
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