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Discrete-Time Neural Observers

Discrete-Time Neural Observers
  • Author : Alma Y. Alanis,Edgar N Sanchez
  • Publisher :Unknown
  • Release Date :2017-02-06
  • Total pages :150
  • ISBN : 9780128105443
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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

Discrete-Time High Order Neural Control

Discrete-Time High Order Neural Control
  • Author : Edgar N. Sanchez,Alma Y. Alanís,Alexander G. Loukianov
  • Publisher :Unknown
  • Release Date :2008-06-24
  • Total pages :110
  • ISBN : 9783540782896
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Summary : 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
  • Author : Jason Michael Stumfoll
  • Publisher :Unknown
  • Release Date :2015
  • Total pages :104
  • ISBN : OCLC:1104294253
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Summary : "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
  • Author : Alma Y. Alanis,Nancy Arana-Daniel,Carlos Lopez-Franco
  • Publisher :Unknown
  • Release Date :2019-03-15
  • Total pages :224
  • ISBN : 9780128182475
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Summary : 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

Discrete-Time Recurrent Neural Control

Discrete-Time Recurrent Neural Control
  • Author : Edgar N. Sanchez
  • Publisher :Unknown
  • Release Date :2018-09-03
  • Total pages :271
  • ISBN : 9781351377423
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Summary : The book presents recent advances in the theory of neural control for discrete-time nonlinear systems with multiple inputs and multiple outputs. The simulation results that appear in each chapter include rigorous mathematical analyses, based on the Lyapunov approach, to establish its properties. The book contains two sections: the first focuses on the analyses of control techniques; the second is dedicated to illustrating results of real-time applications. It also provides solutions for the output trajectory tracking problem of unknown nonlinear systems based on sliding modes and inverse optimal control scheme. "This book on Discrete-time Recurrent Neural Control is unique in the literature, with new knowledge and information about the new technique of recurrent neural control especially for discrete-time systems. The book is well organized and clearly presented. It will be welcome by a wide range of researchers in science and engineering, especially graduate students and junior researchers who want to learn the new notion of recurrent neural control. I believe it will have a good market. It is an excellent book after all." — Guanrong Chen, City University of Hong Kong "This book includes very relevant topics, about neural control. In these days, Artificial Neural Networks have been recovering their relevance and well-stablished importance, this due to its great capacity to process big amounts of data. Artificial Neural Networks development always is related to technological advancements; therefore, it is not a surprise that now we are being witnesses of this new era in Artificial Neural Networks, however most of the developments in this research area only focuses on applicability of the proposed schemes. However, Edgar N. Sanchez author of this book does not lose focus and include both important applications as well as a deep theoretical analysis of Artificial Neural Networks to control discrete-time nonlinear systems. It is important to remark that first, the considered Artificial Neural Networks are development in discrete-time this simplify its implementation in real-time; secondly, the proposed applications ranging from modelling of unknown discrete-time on linear systems to control electrical machines with an emphasize to renewable energy systems. However, its applications are not limited to these kind of systems, due to their theoretical foundation it can be applicable to a large class of nonlinear systems. All of these is supported by the solid research done by the author." — Alma Y. Alanis, University of Guadalajara, Mexico "This book discusses in detail; how neural networks can be used for optimal as well as robust control design. Design of neural network controllers for real time applications such as induction motors, boost converters, inverted pendulum and doubly fed induction generators has also been carried out which gives the book an edge over other similar titles. This book will be an asset for the novice to the experienced ones." — Rajesh Joseph Abraham, Indian Institute of Space Science & Technology, Thiruvananthapuram, India

Neural Network Control of Nonlinear Discrete-Time Systems

Neural Network Control of Nonlinear Discrete-Time Systems
  • Author : Jagannathan Sarangapani
  • Publisher :Unknown
  • Release Date :2018-10-03
  • Total pages :624
  • ISBN : 9781420015454
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Summary : 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.

Neural Networks Modeling and Control

Neural Networks Modeling and Control
  • Author : Jorge D. Rios,Alma Y. Alanis,Nancy Arana-Daniel,Carlos Lopez-Franco
  • Publisher :Unknown
  • Release Date :2020-01-15
  • Total pages :158
  • ISBN : 9780128170793
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Summary : 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

Artificial Higher Order Neural Networks for Modeling and Simulation

Artificial Higher Order Neural Networks for Modeling and Simulation
  • Author : Zhang, Ming
  • Publisher :Unknown
  • Release Date :2012-10-31
  • Total pages :454
  • ISBN : 9781466621763
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Summary : "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
  • Author : IEEE Power Engineering Society. Summer Meeting
  • Publisher :Unknown
  • Release Date :2000
  • Total pages :2607
  • ISBN : 078036421X
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Summary :

Network and Communication Technology Innovations for Web and IT Advancement

Network and Communication Technology Innovations for Web and IT Advancement
  • Author : Alkhatib, Ghazi I.
  • Publisher :Unknown
  • Release Date :2012-10-31
  • Total pages :355
  • ISBN : 9781466621589
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Summary : 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.

Neural Information Processing

Neural Information Processing
  • Author : Minho Lee,Akira Hirose,Zeng-Guang Hou,Rhee Man Kil
  • Publisher :Unknown
  • Release Date :2013-10-29
  • Total pages :646
  • ISBN : 9783642420542
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Summary : 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.

Systems, Automation and Control

Systems, Automation and Control
  • Author : Nabil Derbel,Faouzi Derbel,Olfa Kanoun
  • Publisher :Unknown
  • Release Date :2017-12-04
  • Total pages :270
  • ISBN : 9783110468502
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Summary : The fifth volume of the Series Advances in Systems, Signals and Devices, is dedicated to fields related to Systems, Automation and Control. The scope of this issue encompasses all aspects of the research, development and applications of the science and technology in these fields. Topics of this issue concern: system design, system identification, biological and economical models & control, modern control theory, nonlinear observers, control and application of chaos, adaptive/non-adaptive backstepping control techniques, advances in linear control theory, systems optimization, multivariable control, large scale and infinite dimension systems, nonlinear control, distributed control, predictive control, geometric control, adaptive control, optimal and stochastic control, robust control, neural control, fuzzy control, intelligent control systems, diagnostics, fault tolerant control, robotics and mechatronics, navigation, robotics and human-machine interaction, hierarchical and man-machine systems, etc. Authors are encouraged to submit novel contributions which include results of research or experimental work discussing new developments in the field of systems, automation and control. The series can be also addressed for editing special issues for novel developments in specific fields. The aim of this volume is to promote an international scientific progress in the fields of systems, automation and control. It provides at the same time an opportunity to be informed about interesting results that have been reported during the international SSD conferences.

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 :Unknown
  • Release Date :2020-09-26
  • Total pages :207
  • ISBN : 9783030579579
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Summary : 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 book offers readers a good understanding of how to establish discrete-time tracking control schemes for fixed-wing UAV systems subject to system uncertainties, external wind disturbances and input saturation. It represents a valuable reference guide for academic research on uncertain UAV systems, and can also support advanced / Ph.D. studies on control theory and engineering.

1997 IEEE International Conference on Intelligent Process Systems.

1997 IEEE International Conference on Intelligent Process Systems.
  • Author : Anonim
  • Publisher :Unknown
  • Release Date :1997
  • Total pages :941
  • ISBN : 7800034100
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Summary :

Neural Network Control of Nonlinear Discrete-Time Systems

Neural Network Control of Nonlinear Discrete-Time Systems
  • Author : Jagannathan Sarangapani
  • Publisher :Unknown
  • Release Date :2018-10-03
  • Total pages :624
  • ISBN : 9781420015454
GET BOOK HERE

Summary : 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.

1997 IEEE International Conference on Intelligent Processing Systems ; [papers]

1997 IEEE International Conference on Intelligent Processing Systems ; [papers]
  • Author : IEEE Industrial Electronics Society
  • Publisher :Unknown
  • Release Date :1997
  • Total pages :1893
  • ISBN : 7800034100
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Summary :

Scientia Iranica

Scientia Iranica
  • Author : Anonim
  • Publisher :Unknown
  • Release Date :2004
  • Total pages :229
  • ISBN : UOM:39015081473442
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Summary :

Index to IEEE Publications

Index to IEEE Publications
  • Author : Institute of Electrical and Electronics Engineers
  • Publisher :Unknown
  • Release Date :1996
  • Total pages :229
  • ISBN : STANFORD:36105021103101
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Summary :

Computer & Control Abstracts

Computer & Control Abstracts
  • Author : Anonim
  • Publisher :Unknown
  • Release Date :1996
  • Total pages :229
  • ISBN : UOM:39015039842029
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Summary :

Joint 9th IFSA World Congress and 20th NAFIPS International Conference

Joint 9th IFSA World Congress and 20th NAFIPS International Conference
  • Author : International Fuzzy Systems Association
  • Publisher :Unknown
  • Release Date :2001
  • Total pages :3100
  • ISBN : UOM:39015049110300
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Summary :

Journal of Dynamic Systems, Measurement, and Control

Journal of Dynamic Systems, Measurement, and Control
  • Author : Anonim
  • Publisher :Unknown
  • Release Date :2001
  • Total pages :229
  • ISBN : UCSD:31822020350179
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Summary :