Discrete Time Recurrent Neural Control

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Discrete-Time Recurrent Neural Control

Edgar N. Sanchez
Discrete Time Recurrent Neural Control Book PDF

Author : Edgar N. Sanchez
Publisher : CRC Press
Page : 205 pages
File Size : 43,7 Mb
Release : 2018-09-03
Category : Technology & Engineering
ISBN : 9781351377423

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Discrete-Time Recurrent Neural Control by Edgar N. Sanchez Pdf

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

Discrete-Time High Order Neural Control

Edgar N. Sanchez,Alma Y. Alanís,Alexander G. Loukianov
Discrete Time High Order Neural Control Book PDF

Author : Edgar N. Sanchez,Alma Y. Alanís,Alexander G. Loukianov
Publisher : Springer
Page : 110 pages
File Size : 41,6 Mb
Release : 2008-06-24
Category : Technology & Engineering
ISBN : 9783540782896

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Discrete-Time High Order Neural Control by Edgar N. Sanchez,Alma Y. Alanís,Alexander G. Loukianov Pdf

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 High Order Neural Control

Edgar N. Sanchez,Alma Y. Alanís,Alexander G. Loukianov
Discrete Time High Order Neural Control Book PDF

Author : Edgar N. Sanchez,Alma Y. Alanís,Alexander G. Loukianov
Publisher : Springer Science & Business Media
Page : 116 pages
File Size : 50,9 Mb
Release : 2008-04-29
Category : Mathematics
ISBN : 9783540782889

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Discrete-Time High Order Neural Control by Edgar N. Sanchez,Alma Y. Alanís,Alexander G. Loukianov Pdf

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 Inverse Optimal Control for Nonlinear Systems

Edgar N. Sanchez,Fernando Ornelas-Tellez
Discrete Time Inverse Optimal Control for Nonlinear Systems Book PDF

Author : Edgar N. Sanchez,Fernando Ornelas-Tellez
Publisher : CRC Press
Page : 268 pages
File Size : 41,8 Mb
Release : 2017-12-19
Category : Technology & Engineering
ISBN : 9781466580886

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Discrete-Time Inverse Optimal Control for Nonlinear Systems by Edgar N. Sanchez,Fernando Ornelas-Tellez Pdf

Discrete-Time Inverse Optimal Control for Nonlinear Systems proposes a novel inverse optimal control scheme for stabilization and trajectory tracking of discrete-time nonlinear systems. This avoids the need to solve the associated Hamilton-Jacobi-Bellman equation and minimizes a cost functional, resulting in a more efficient controller. Design More Efficient Controllers for Stabilization and Trajectory Tracking of Discrete-Time Nonlinear Systems The book presents two approaches for controller synthesis: the first based on passivity theory and the second on a control Lyapunov function (CLF). The synthesized discrete-time optimal controller can be directly implemented in real-time systems. The book also proposes the use of recurrent neural networks to model discrete-time nonlinear systems. Combined with the inverse optimal control approach, such models constitute a powerful tool to deal with uncertainties such as unmodeled dynamics and disturbances. Learn from Simulations and an In-Depth Case Study The authors include a variety of simulations to illustrate the effectiveness of the synthesized controllers for stabilization and trajectory tracking of discrete-time nonlinear systems. An in-depth case study applies the control schemes to glycemic control in patients with type 1 diabetes mellitus, to calculate the adequate insulin delivery rate required to prevent hyperglycemia and hypoglycemia levels. The discrete-time optimal and robust control techniques proposed can be used in a range of industrial applications, from aerospace and energy to biomedical and electromechanical systems. Highlighting optimal and efficient control algorithms, this is a valuable resource for researchers, engineers, and students working in nonlinear system control.

Decentralized Neural Control: Application to Robotics

Ramon Garcia-Hernandez,Michel Lopez-Franco,Edgar N. Sanchez,Alma y. Alanis,Jose A. Ruz-Hernandez
Decentralized Neural Control Application to Robotics Book PDF

Author : Ramon Garcia-Hernandez,Michel Lopez-Franco,Edgar N. Sanchez,Alma y. Alanis,Jose A. Ruz-Hernandez
Publisher : Springer
Page : 111 pages
File Size : 50,8 Mb
Release : 2017-02-05
Category : Technology & Engineering
ISBN : 9783319533124

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Decentralized Neural Control: Application to Robotics by Ramon Garcia-Hernandez,Michel Lopez-Franco,Edgar N. Sanchez,Alma y. Alanis,Jose A. Ruz-Hernandez Pdf

This book provides a decentralized approach for the identification and control of robotics systems. It also presents recent research in decentralized neural control and includes applications to robotics. Decentralized control is free from difficulties due to complexity in design, debugging, data gathering and storage requirements, making it preferable for interconnected systems. Furthermore, as opposed to the centralized approach, it can be implemented with parallel processors. This approach deals with four decentralized control schemes, which are able to identify the robot dynamics. The training of each neural network is performed on-line using an extended Kalman filter (EKF). The first indirect decentralized control scheme applies the discrete-time block control approach, to formulate a nonlinear sliding manifold. The second direct decentralized neural control scheme is based on the backstepping technique, approximated by a high order neural network. The third control scheme applies a decentralized neural inverse optimal control for stabilization. The fourth decentralized neural inverse optimal control is designed for trajectory tracking. This comprehensive work on decentralized control of robot manipulators and mobile robots is intended for professors, students and professionals wanting to understand and apply advanced knowledge in their field of work.

Discrete-Time Neural Observers

Alma Y Alanis,Edgar N. Sanchez
Discrete Time Neural Observers Book PDF

Author : Alma Y Alanis,Edgar N. Sanchez
Publisher : Academic Press
Page : 152 pages
File Size : 45,7 Mb
Release : 2017-02-06
Category : Computers
ISBN : 9780128105443

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Discrete-Time Neural Observers by Alma Y Alanis,Edgar N. Sanchez Pdf

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

Neural Network Control of Nonlinear Discrete-Time Systems

Jagannathan Sarangapani
Neural Network Control of Nonlinear Discrete Time Systems Book PDF

Author : Jagannathan Sarangapani
Publisher : CRC Press
Page : 624 pages
File Size : 49,6 Mb
Release : 2018-10-03
Category : Technology & Engineering
ISBN : 9781420015454

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Neural Network Control of Nonlinear Discrete-Time Systems by Jagannathan Sarangapani Pdf

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.

Applied Artificial Higher Order Neural Networks for Control and Recognition

Zhang, Ming
Applied Artificial Higher Order Neural Networks for Control and Recognition Book PDF

Author : Zhang, Ming
Publisher : IGI Global
Page : 511 pages
File Size : 51,7 Mb
Release : 2016-05-05
Category : Computers
ISBN : 9781522500643

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Applied Artificial Higher Order Neural Networks for Control and Recognition by Zhang, Ming Pdf

In recent years, Higher Order Neural Networks (HONNs) have been widely adopted by researchers for applications in control signal generating, pattern recognition, nonlinear recognition, classification, and predition of control and recognition scenarios. Due to the fact that HONNs have been proven to be faster, more accurate, and easier to explain than traditional neural networks, their applications are limitless. Applied Artificial Higher Order Neural Networks for Control and Recognition explores the ways in which higher order neural networks are being integrated specifically for intelligent technology applications. Emphasizing emerging research, practice, and real-world implementation, this timely reference publication is an essential reference source for researchers, IT professionals, and graduate-level computer science and engineering students.

Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications

Oscar Castillo,Patricia Melin,Janusz Kacprzyk
Intuitionistic and Type 2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms Theory and Applications Book PDF

Author : Oscar Castillo,Patricia Melin,Janusz Kacprzyk
Publisher : Springer Nature
Page : 792 pages
File Size : 52,7 Mb
Release : 2020-02-27
Category : Technology & Engineering
ISBN : 9783030354459

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Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications by Oscar Castillo,Patricia Melin,Janusz Kacprzyk Pdf

This book describes the latest advances in fuzzy logic, neural networks, and optimization algorithms, as well as their hybrid intelligent combinations, and their applications in the areas such as intelligent control, robotics, pattern recognition, medical diagnosis, time series prediction, and optimization. The topic is highly relevant as most current intelligent systems and devices use some form of intelligent feature to enhance their performance. The book also presents new and advanced models and algorithms of type-2 fuzzy logic and intuitionistic fuzzy systems, which are of great interest to researchers in these areas. Further, it proposes novel, nature-inspired optimization algorithms and innovative neural models. Featuring contributions on theoretical aspects as well as applications, the book appeals to a wide audience.

Neural Control of Renewable Electrical Power Systems

Edgar N. Sánchez,Larbi Djilali
Neural Control of Renewable Electrical Power Systems Book PDF

Author : Edgar N. Sánchez,Larbi Djilali
Publisher : Springer Nature
Page : 206 pages
File Size : 41,6 Mb
Release : 2020-05-09
Category : Technology & Engineering
ISBN : 9783030474430

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Neural Control of Renewable Electrical Power Systems by Edgar N. Sánchez,Larbi Djilali Pdf

This book presents advanced control techniques that use neural networks to deal with grid disturbances in the context renewable energy sources, and to enhance low-voltage ride-through capacity, which is a vital in terms of ensuring that the integration of distributed energy resources into the electrical power network. It presents modern control algorithms based on neural identification for different renewable energy sources, such as wind power, which uses doubly-fed induction generators, solar power, and battery banks for storage. It then discusses the use of the proposed controllers to track doubly-fed induction generator dynamics references: DC voltage, grid power factor, and stator active and reactive power, and the use of simulations to validate their performance. Further, it addresses methods of testing low-voltage ride-through capacity enhancement in the presence of grid disturbances, as well as the experimental validation of the controllers under both normal and abnormal grid conditions. The book then describes how the proposed control schemes are extended to control a grid-connected microgrid, and the use of an IEEE 9-bus system to evaluate their performance and response in the presence of grid disturbances. Lastly, it examines the real-time simulation of the entire system under normal and abnormal conditions using an Opal-RT simulator.

Nature-Inspired Computing: Concepts, Methodologies, Tools, and Applications

Management Association, Information Resources
Nature Inspired Computing Concepts Methodologies Tools and Applications Book PDF

Author : Management Association, Information Resources
Publisher : IGI Global
Page : 1780 pages
File Size : 47,9 Mb
Release : 2016-07-26
Category : Computers
ISBN : 9781522507895

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Nature-Inspired Computing: Concepts, Methodologies, Tools, and Applications by Management Association, Information Resources Pdf

As technology continues to become more sophisticated, mimicking natural processes and phenomena also becomes more of a reality. Continued research in the field of natural computing enables an understanding of the world around us, in addition to opportunities for man-made computing to mirror the natural processes and systems that have existed for centuries. Nature-Inspired Computing: Concepts, Methodologies, Tools, and Applications takes an interdisciplinary approach to the topic of natural computing, including emerging technologies being developed for the purpose of simulating natural phenomena, applications across industries, and the future outlook of biologically and nature-inspired technologies. Emphasizing critical research in a comprehensive multi-volume set, this publication is designed for use by IT professionals, researchers, and graduate students studying intelligent computing.

Human-Robot Interaction Control Using Reinforcement Learning

Wen Yu,Adolfo Perrusquia
Human Robot Interaction Control Using Reinforcement Learning Book PDF

Author : Wen Yu,Adolfo Perrusquia
Publisher : John Wiley & Sons
Page : 290 pages
File Size : 48,7 Mb
Release : 2021-10-19
Category : Technology & Engineering
ISBN : 9781119782742

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Human-Robot Interaction Control Using Reinforcement Learning by Wen Yu,Adolfo Perrusquia Pdf

A comprehensive exploration of the control schemes of human-robot interactions In Human-Robot Interaction Control Using Reinforcement Learning, an expert team of authors delivers a concise overview of human-robot interaction control schemes and insightful presentations of novel, model-free and reinforcement learning controllers. The book begins with a brief introduction to state-of-the-art human-robot interaction control and reinforcement learning before moving on to describe the typical environment model. The authors also describe some of the most famous identification techniques for parameter estimation. Human-Robot Interaction Control Using Reinforcement Learning offers rigorous mathematical treatments and demonstrations that facilitate the understanding of control schemes and algorithms. It also describes stability and convergence analysis of human-robot interaction control and reinforcement learning based control. The authors also discuss advanced and cutting-edge topics, like inverse and velocity kinematics solutions, H2 neural control, and likely upcoming developments in the field of robotics. Readers will also enjoy: A thorough introduction to model-based human-robot interaction control Comprehensive explorations of model-free human-robot interaction control and human-in-the-loop control using Euler angles Practical discussions of reinforcement learning for robot position and force control, as well as continuous time reinforcement learning for robot force control In-depth examinations of robot control in worst-case uncertainty using reinforcement learning and the control of redundant robots using multi-agent reinforcement learning Perfect for senior undergraduate and graduate students, academic researchers, and industrial practitioners studying and working in the fields of robotics, learning control systems, neural networks, and computational intelligence, Human-Robot Interaction Control Using Reinforcement Learning is also an indispensable resource for students and professionals studying reinforcement learning.

Advances in Neural Networks -- ISNN 2010

James Kwok,Bao-Liang Lu,Liqing Zhang
Advances in Neural Networks ISNN 2010 Book PDF

Author : James Kwok,Bao-Liang Lu,Liqing Zhang
Publisher : Springer
Page : 757 pages
File Size : 44,6 Mb
Release : 2010-05-30
Category : Computers
ISBN : 9783642132780

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Advances in Neural Networks -- ISNN 2010 by James Kwok,Bao-Liang Lu,Liqing Zhang Pdf

This book and its sister volume collect refereed papers presented at the 7th Inter- tional Symposium on Neural Networks (ISNN 2010), held in Shanghai, China, June 6-9, 2010. Building on the success of the previous six successive ISNN symposiums, ISNN has become a well-established series of popular and high-quality conferences on neural computation and its applications. ISNN aims at providing a platform for scientists, researchers, engineers, as well as students to gather together to present and discuss the latest progresses in neural networks, and applications in diverse areas. Nowadays, the field of neural networks has been fostered far beyond the traditional artificial neural networks. This year, ISNN 2010 received 591 submissions from more than 40 countries and regions. Based on rigorous reviews, 170 papers were selected for publication in the proceedings. The papers collected in the proceedings cover a broad spectrum of fields, ranging from neurophysiological experiments, neural modeling to extensions and applications of neural networks. We have organized the papers into two volumes based on their topics. The first volume, entitled “Advances in Neural Networks- ISNN 2010, Part 1,” covers the following topics: neurophysiological foundation, theory and models, learning and inference, neurodynamics. The second volume en- tled “Advance in Neural Networks ISNN 2010, Part 2” covers the following five topics: SVM and kernel methods, vision and image, data mining and text analysis, BCI and brain imaging, and applications.

Neural Networks Modeling and Control

Jorge D. Rios,Alma Y Alanis,Nancy Arana-Daniel,Carlos Lopez-Franco
Neural Networks Modeling and Control Book PDF

Author : Jorge D. Rios,Alma Y Alanis,Nancy Arana-Daniel,Carlos Lopez-Franco
Publisher : Academic Press
Page : 160 pages
File Size : 48,5 Mb
Release : 2020-01-15
Category : Science
ISBN : 9780128170793

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Neural Networks Modeling and Control by Jorge D. Rios,Alma Y Alanis,Nancy Arana-Daniel,Carlos Lopez-Franco Pdf

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 Computer Science and Engineering: Trends for Emerging Applications

Zhang, Ming
Artificial Higher Order Neural Networks for Computer Science and Engineering Trends for Emerging Applications Book PDF

Author : Zhang, Ming
Publisher : IGI Global
Page : 660 pages
File Size : 47,6 Mb
Release : 2010-02-28
Category : Computers
ISBN : 9781615207121

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Artificial Higher Order Neural Networks for Computer Science and Engineering: Trends for Emerging Applications by Zhang, Ming Pdf

"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.