Discrete Time High Order Neural Control

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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 : 45,8 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 : 51,7 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 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 : 52,7 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

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 : 47,9 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.

Artificial Higher Order Neural Networks for Modeling and Simulation

Zhang, Ming
Artificial Higher Order Neural Networks for Modeling and Simulation Book PDF

Author : Zhang, Ming
Publisher : IGI Global
Page : 455 pages
File Size : 49,7 Mb
Release : 2012-10-31
Category : Computers
ISBN : 9781466621763

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Artificial Higher Order Neural Networks for Modeling and Simulation by Zhang, Ming Pdf

"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.

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 : 43,8 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.

Emerging Capabilities and Applications of Artificial Higher Order Neural Networks

Zhang, Ming
Emerging Capabilities and Applications of Artificial Higher Order Neural Networks Book PDF

Author : Zhang, Ming
Publisher : IGI Global
Page : 540 pages
File Size : 54,8 Mb
Release : 2021-02-05
Category : Computers
ISBN : 9781799835653

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Emerging Capabilities and Applications of Artificial Higher Order Neural Networks by Zhang, Ming Pdf

Artificial neural network research is one of the new directions for new generation computers. Current research suggests that open box artificial higher order neural networks (HONNs) play an important role in this new direction. HONNs will challenge traditional artificial neural network products and change the research methodology that people are currently using in control and recognition areas for the control signal generating, pattern recognition, nonlinear recognition, classification, and prediction. Since HONNs are open box models, they can be easily accepted and used by individuals working in information science, information technology, management, economics, and business fields. Emerging Capabilities and Applications of Artificial Higher Order Neural Networks contains innovative research on how to use HONNs in control and recognition areas and explains why HONNs can approximate any nonlinear data to any degree of accuracy, their ease of use, and how they can have better nonlinear data recognition accuracy than SAS nonlinear procedures. Featuring coverage on a broad range of topics such as nonlinear regression, pattern recognition, and data prediction, this book is ideally designed for data analysists, IT specialists, engineers, researchers, academics, students, and professionals working in the fields of economics, business, modeling, simulation, control, recognition, computer science, and engineering research.

Neural Networks for Robotics

Nancy Arana-Daniel,Alma Y. Alanis,Carlos Lopez-Franco
Neural Networks for Robotics Book PDF

Author : Nancy Arana-Daniel,Alma Y. Alanis,Carlos Lopez-Franco
Publisher : CRC Press
Page : 289 pages
File Size : 49,6 Mb
Release : 2018-08-21
Category : Technology & Engineering
ISBN : 9781351231770

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Neural Networks for Robotics by Nancy Arana-Daniel,Alma Y. Alanis,Carlos Lopez-Franco Pdf

The book offers an insight on artificial neural networks for giving a robot a high level of autonomous tasks, such as navigation, cost mapping, object recognition, intelligent control of ground and aerial robots, and clustering, with real-time implementations. The reader will learn various methodologies that can be used to solve each stage on autonomous navigation for robots, from object recognition, clustering of obstacles, cost mapping of environments, path planning, and vision to low level control. These methodologies include real-life scenarios to implement a wide range of artificial neural network architectures.

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 : 53,5 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.

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 : 51,9 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.

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 : 48,9 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.

Network and Communication Technology Innovations for Web and IT Advancement

Alkhatib, Ghazi I.
Network and Communication Technology Innovations for Web and IT Advancement Book PDF

Author : Alkhatib, Ghazi I.
Publisher : IGI Global
Page : 455 pages
File Size : 48,7 Mb
Release : 2012-10-31
Category : Computers
ISBN : 9781466621589

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Network and Communication Technology Innovations for Web and IT Advancement by Alkhatib, Ghazi I. Pdf

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.

Artificial Neural Networks for Engineering Applications

Alma Y. Alanis,Nancy Arana-Daniel,Carlos Lopez-Franco
Artificial Neural Networks for Engineering Applications Book PDF

Author : Alma Y. Alanis,Nancy Arana-Daniel,Carlos Lopez-Franco
Publisher : Academic Press
Page : 176 pages
File Size : 55,9 Mb
Release : 2019-02-07
Category : Science
ISBN : 9780128182482

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Artificial Neural Networks for Engineering Applications by Alma Y. Alanis,Nancy Arana-Daniel,Carlos Lopez-Franco Pdf

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 Information Processing

Derong Liu,Shengli Xie,Yuanqing Li,Dongbin Zhao,El-Sayed M. El-Alfy
Neural Information Processing Book PDF

Author : Derong Liu,Shengli Xie,Yuanqing Li,Dongbin Zhao,El-Sayed M. El-Alfy
Publisher : Springer
Page : 912 pages
File Size : 51,5 Mb
Release : 2017-11-07
Category : Computers
ISBN : 9783319701363

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Neural Information Processing by Derong Liu,Shengli Xie,Yuanqing Li,Dongbin Zhao,El-Sayed M. El-Alfy Pdf

The six volume set LNCS 10634, LNCS 10635, LNCS 10636, LNCS 10637, LNCS 10638, and LNCS 10639 constitues the proceedings of the 24rd International Conference on Neural Information Processing, ICONIP 2017, held in Guangzhou, China, in November 2017. The 563 full papers presented were carefully reviewed and selected from 856 submissions. The 6 volumes are organized in topical sections on Machine Learning, Reinforcement Learning, Big Data Analysis, Deep Learning, Brain-Computer Interface, Computational Finance, Computer Vision, Neurodynamics, Sensory Perception and Decision Making, Computational Intelligence, Neural Data Analysis, Biomedical Engineering, Emotion and Bayesian Networks, Data Mining, Time-Series Analysis, Social Networks, Bioinformatics, Information Security and Social Cognition, Robotics and Control, Pattern Recognition, Neuromorphic Hardware and Speech Processing.

Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications

Management Association, Information Resources
Deep Learning and Neural Networks Concepts Methodologies Tools and Applications Book PDF

Author : Management Association, Information Resources
Publisher : IGI Global
Page : 1671 pages
File Size : 54,9 Mb
Release : 2019-10-11
Category : Computers
ISBN : 9781799804154

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Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications by Management Association, Information Resources Pdf

Due to the growing use of web applications and communication devices, the use of data has increased throughout various industries. It is necessary to develop new techniques for managing data in order to ensure adequate usage. Deep learning, a subset of artificial intelligence and machine learning, has been recognized in various real-world applications such as computer vision, image processing, and pattern recognition. The deep learning approach has opened new opportunities that can make such real-life applications and tasks easier and more efficient. Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications is a vital reference source that trends in data analytics and potential technologies that will facilitate insight in various domains of science, industry, business, and consumer applications. It also explores the latest concepts, algorithms, and techniques of deep learning and data mining and analysis. Highlighting a range of topics such as natural language processing, predictive analytics, and deep neural networks, this multi-volume book is ideally designed for computer engineers, software developers, IT professionals, academicians, researchers, and upper-level students seeking current research on the latest trends in the field of deep learning.