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Artificial Neural Networks for Modelling and Control of Non-Linear Systems

Artificial Neural Networks for Modelling and Control of Non-Linear Systems
A Book

by Johan A.K. Suykens,Joos P.L. Vandewalle,B.L. de Moor

  • Publisher : Springer Science & Business Media
  • Release : 2012-12-06
  • Pages : 235
  • ISBN : 1475724934
  • Language : En, Es, Fr & De
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Artificial neural networks possess several properties that make them particularly attractive for applications to modelling and control of complex non-linear systems. Among these properties are their universal approximation ability, their parallel network structure and the availability of on- and off-line learning methods for the interconnection weights. However, dynamic models that contain neural network architectures might be highly non-linear and difficult to analyse as a result. Artificial Neural Networks for Modelling and Control of Non-Linear Systems investigates the subject from a system theoretical point of view. However the mathematical theory that is required from the reader is limited to matrix calculus, basic analysis, differential equations and basic linear system theory. No preliminary knowledge of neural networks is explicitly required. The book presents both classical and novel network architectures and learning algorithms for modelling and control. Topics include non-linear system identification, neural optimal control, top-down model based neural control design and stability analysis of neural control systems. A major contribution of this book is to introduce NLq Theory as an extension towards modern control theory, in order to analyze and synthesize non-linear systems that contain linear together with static non-linear operators that satisfy a sector condition: neural state space control systems are an example. Moreover, it turns out that NLq Theory is unifying with respect to many problems arising in neural networks, systems and control. Examples show that complex non-linear systems can be modelled and controlled within NLq theory, including mastering chaos. The didactic flavor of this book makes it suitable for use as a text for a course on Neural Networks. In addition, researchers and designers will find many important new techniques, in particular NLq emTheory, that have applications in control theory, system theory, circuit theory and Time Series Analysis.

Neural Networks for Control

Neural Networks for Control
A Book

by W. Thomas Miller,Paul J. Werbos,Richard S. Sutton

  • Publisher : MIT Press
  • Release : 1995
  • Pages : 544
  • ISBN : 9780262631617
  • Language : En, Es, Fr & De
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Neural Networks for Control highlights key issues in learning control and identifiesresearch directions that could lead to practical solutions for control problems in criticalapplication domains. It addresses general issues of neural network based control and neural networklearning with regard to specific problems of motion planning and control in robotics, and takes upapplication domains well suited to the capabilities of neural network controllers. The appendixdescribes seven benchmark control problems.W. Thomas Miller, III is Professor of Electrical andComputer Engineering at the University of New Hampshire. Richard S. Sutton works for GTELaboratories Incorporated. Paul J. Werbos is Program Director for Neuroengineering at the NationalScience Foundation.Contributors: Andrew G. Barto. Ronald J. Williams. Paul J. Werbos. Kumpati S.Narendra. L. Gordon Kraft, III, David P. Campagna. Mitsuo Kawato. Bartlett W. Met. Christopher G.Atkeson, David J. Reinkensmeyer. Derrick Nguyen, Bernard Widrow. James C. Houk, Satinder P. Singh,Charles Fisher. Judy A. Franklin, Oliver G. Selfridge. Arthur C. Sanderson. Lyle H. Ungar. CharlesC. Jorgensen, C. Schley. Martin Herman, James S. Albus, Tsai-Hong Hong. Charles W. Anderson, W.Thomas Miller, III.

Process Modeling and Control of Enhanced Coagulation

Process Modeling and Control of Enhanced Coagulation
A Book

by Stephen John Stanley

  • Publisher : American Water Works Association
  • Release : 2000-01-01
  • Pages : 145
  • ISBN : 1583210504
  • Language : En, Es, Fr & De
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A Comprehensive Guide to Neural Network Modeling

A Comprehensive Guide to Neural Network Modeling
A Book

by Steffen Skaar

  • Publisher : Nova Science Publishers
  • Release : 2020-10-26
  • Pages : 172
  • ISBN : 9781536185423
  • Language : En, Es, Fr & De
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As artificial neural networks have been gaining importance in the field of engineering, this compilation aims to review the scientific literature regarding the use of artificial neural networks for the modelling and optimization of food drying processes. The applications of artificial neural networks in food engineering are presented, particularly focusing on control, monitoring and modeling of industrial food processes.The authors emphasize the main achievements of artificial neural network modeling in recent years in the field of quantitative structure-activity relationships and quantitative structure-retention relationships.In the closing study, artificial intelligence techniques are applied to river water quality data and artificial intelligence models are developed in an effort to contribute to the reduction of the cost of future on-line measurement stations.

Neural Networks in Robotics

Neural Networks in Robotics
A Book

by George A. Bekey,Kenneth Y. Goldberg

  • Publisher : Springer Science & Business Media
  • Release : 2012-12-06
  • Pages : 563
  • ISBN : 1461531802
  • Language : En, Es, Fr & De
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Neural Networks in Robotics is the first book to present an integrated view of both the application of artificial neural networks to robot control and the neuromuscular models from which robots were created. The behavior of biological systems provides both the inspiration and the challenge for robotics. The goal is to build robots which can emulate the ability of living organisms to integrate perceptual inputs smoothly with motor responses, even in the presence of novel stimuli and changes in the environment. The ability of living systems to learn and to adapt provides the standard against which robotic systems are judged. In order to emulate these abilities, a number of investigators have attempted to create robot controllers which are modelled on known processes in the brain and musculo-skeletal system. Several of these models are described in this book. On the other hand, connectionist (artificial neural network) formulations are attractive for the computation of inverse kinematics and dynamics of robots, because they can be trained for this purpose without explicit programming. Some of the computational advantages and problems of this approach are also presented. For any serious student of robotics, Neural Networks in Robotics provides an indispensable reference to the work of major researchers in the field. Similarly, since robotics is an outstanding application area for artificial neural networks, Neural Networks in Robotics is equally important to workers in connectionism and to students for sensormonitor control in living systems.

Neural Networks for Modelling and Control of Dynamic Systems

Neural Networks for Modelling and Control of Dynamic Systems
A Practitioner's Handbook

by M. Norgaard

  • Publisher : Unknown Publisher
  • Release : 2003
  • Pages : 246
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Fuzzy Systems

Fuzzy Systems
Modeling and Control

by Hung T. Nguyen,Michio Sugeno

  • Publisher : Springer Science & Business Media
  • Release : 1998-07-31
  • Pages : 519
  • ISBN : 9780792380641
  • Language : En, Es, Fr & De
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The analysis and control of complex systems have been the main motivation for the emergence of fuzzy set theory since its inception. It is also a major research field where many applications, especially industrial ones, have made fuzzy logic famous. This unique handbook is devoted to an extensive, organized, and up-to-date presentation of fuzzy systems engineering methods. The book includes detailed material and extensive bibliographies, written by leading experts in the field, on topics such as: Use of fuzzy logic in various control systems. Fuzzy rule-based modeling and its universal approximation properties. Learning and tuning techniques for fuzzy models, using neural networks and genetic algorithms. Fuzzy control methods, including issues such as stability analysis and design techniques, as well as the relationship with traditional linear control. Fuzzy sets relation to the study of chaotic systems, and the fuzzy extension of set-valued approaches to systems modeling through the use of differential inclusions. Fuzzy Systems: Modeling and Control is part of The Handbooks of Fuzzy Sets Series. The series provides a complete picture of contemporary fuzzy set theory and its applications. This volume is a key reference for systems engineers and scientists seeking a guide to the vast amount of literature in fuzzy logic modeling and control.

Neurofuzzy Adaptive Modelling and Control

Neurofuzzy Adaptive Modelling and Control
A Book

by Martin Brown,Harris Chris,Chris Harris

  • Publisher : Unknown Publisher
  • Release : 1994
  • Pages : 508
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Describes several adaptive neural and fuzzy networks and introduces the associate memory class of systems. The Albus CMAC, the B-spline network and a class of fuzzy systems are described and analyzed. Their desirable features, such as local learning, are stressed and the algorithms are evaluated.

Modeling and Control of Uncertain Nonlinear Systems with Fuzzy Equations and Z-Number

Modeling and Control of Uncertain Nonlinear Systems with Fuzzy Equations and Z-Number
A Book

by Wen Yu,Raheleh Jafari

  • Publisher : John Wiley & Sons
  • Release : 2019-07-02
  • Pages : 208
  • ISBN : 111949155X
  • Language : En, Es, Fr & De
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An original, systematic-solution approach to uncertain nonlinear systems control and modeling using fuzzy equations and fuzzy differential equations There are various numerical and analytical approaches to the modeling and control of uncertain nonlinear systems. Fuzzy logic theory is an increasingly popular method used to solve inconvenience problems in nonlinear modeling. Modeling and Control of Uncertain Nonlinear Systems with Fuzzy Equations and Z-Number presents a structured approach to the control and modeling of uncertain nonlinear systems in industry using fuzzy equations and fuzzy differential equations. The first major work to explore methods based on neural networks and Bernstein neural networks, this innovative volume provides a framework for control and modeling of uncertain nonlinear systems with applications to industry. Readers learn how to use fuzzy techniques to solve scientific and engineering problems and understand intelligent control design and applications. The text assembles the results of four years of research on control of uncertain nonlinear systems with dual fuzzy equations, fuzzy modeling for uncertain nonlinear systems with fuzzy equations, the numerical solution of fuzzy equations with Z-numbers, and the numerical solution of fuzzy differential equations with Z-numbers. Using clear and accessible language to explain concepts and principles applicable to real-world scenarios, this book: Presents the modeling and control of uncertain nonlinear systems with fuzzy equations and fuzzy differential equations Includes an overview of uncertain nonlinear systems for non-specialists Teaches readers to use simulation, modeling and verification skills valuable for scientific research and engineering systems development Reinforces comprehension with illustrations, tables, examples, and simulations Modeling and Control of Uncertain Nonlinear Systems with Fuzzy Equations and Z-Number is suitable as a textbook for advanced students, academic and industrial researchers, and practitioners in fields of systems engineering, learning control systems, neural networks, computational intelligence, and fuzzy logic control.

Artificial Neural Networks in Food Processing

Artificial Neural Networks in Food Processing
Modeling and Predictive Control

by Mohamed Tarek Khadir

  • Publisher : Walter de Gruyter GmbH & Co KG
  • Release : 2021-01-18
  • Pages : 200
  • ISBN : 3110646137
  • Language : En, Es, Fr & De
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Artificial Neural Networks (ANNs) is a powerful computational tool to mimic the learning process of the mammalian brain. This book gives a comprehensive overview of ANNs including an introduction to the topic, classifications of single neurons and neural networks, model predictive control and a review of ANNs used in food processing. Also, examples of ANNs in food processing applications such as pasteurization control are illustrated.

Neural Network Applications in Control

Neural Network Applications in Control
A Book

by Institution of Electrical Engineers

  • Publisher : IET
  • Release : 1995
  • Pages : 295
  • ISBN : 9780852968529
  • Language : En, Es, Fr & De
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Introducing a wide variety of network types, including Kohenen nets, n-tuple nets and radial basis function networks as well as the more useful multilayer perception back-propagation networks, this book aims to give a detailed appreciation of the use of neural nets in these applications.

Advances In Intelligent Control

Advances In Intelligent Control
A Book

by C J Harris

  • Publisher : CRC Press
  • Release : 1994-03-11
  • Pages : 373
  • ISBN : 9780748400669
  • Language : En, Es, Fr & De
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"Advances in intelligent Control" is a collection of essays covering the latest research in the field. Based on a special issue of "The International Journal of Control", the book is arranged in two parts. Part one contains recent contributions of artificial neural networks to modelling and control. Part two concerns itself primarily with aspects of fuzzy logic in intelligent control, guidance and estimation, although some of the contributions either make direct equivalence relationships to neural networks or use hybrid methods where a neural network is used to develop the fuzzy rule base.

Computational Intelligence Applications in Modeling and Control

Computational Intelligence Applications in Modeling and Control
A Book

by Ahmad Taher Azar,Sundarapandian Vaidyanathan

  • Publisher : Springer
  • Release : 2014-12-26
  • Pages : 430
  • ISBN : 3319110179
  • Language : En, Es, Fr & De
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The development of computational intelligence (CI) systems was inspired by observable and imitable aspects of intelligent activity of human being and nature. The essence of the systems based on computational intelligence is to process and interpret data of various nature so that that CI is strictly connected with the increase of available data as well as capabilities of their processing, mutually supportive factors. Developed theories of computational intelligence were quickly applied in many fields of engineering, data analysis, forecasting, biomedicine and others. They are used in images and sounds processing and identifying, signals processing, multidimensional data visualization, steering of objects, analysis of lexicographic data, requesting systems in banking, diagnostic systems, expert systems and many other practical implementations. This book consists of 16 contributed chapters by subject experts who are specialized in the various topics addressed in this book. The special chapters have been brought out in the broad areas of Control Systems, Power Electronics, Computer Science, Information Technology, modeling and engineering applications. Special importance was given to chapters offering practical solutions and novel methods for the recent research problems in the main areas of this book, viz. Control Systems, Modeling, Computer Science, IT and engineering applications. This book will serve as a reference book for graduate students and researchers with a basic knowledge of control theory, computer science and soft-computing techniques. The resulting design procedures are emphasized using Matlab/Simulink software.

Dynamic Neuroscience

Dynamic Neuroscience
Statistics, Modeling, and Control

by Zhe Chen,Sridevi V. Sarma

  • Publisher : Springer
  • Release : 2017-12-27
  • Pages : 327
  • ISBN : 3319719769
  • Language : En, Es, Fr & De
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This book shows how to develop efficient quantitative methods to characterize neural data and extra information that reveals underlying dynamics and neurophysiological mechanisms. Written by active experts in the field, it contains an exchange of innovative ideas among researchers at both computational and experimental ends, as well as those at the interface. Authors discuss research challenges and new directions in emerging areas with two goals in mind: to collect recent advances in statistics, signal processing, modeling, and control methods in neuroscience; and to welcome and foster innovative or cross-disciplinary ideas along this line of research and discuss important research issues in neural data analysis. Making use of both tutorial and review materials, this book is written for neural, electrical, and biomedical engineers; computational neuroscientists; statisticians; computer scientists; and clinical engineers.

Modeling and Control of Complex Systems

Modeling and Control of Complex Systems
A Book

by Petros A. Ioannou,Andreas Pitsillides

  • Publisher : CRC Press
  • Release : 2007-12-26
  • Pages : 544
  • ISBN : 9780849379864
  • Language : En, Es, Fr & De
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Comprehension of complex systems comes from an understanding of not only the behavior of constituent elements but how they act together to form the behavior of the whole. However, given the multidisciplinary nature of complex systems, the scattering of information across different areas creates a chaotic situation for those trying to understand possible solutions and applications. Modeling and Control of Complex Systems brings together a number of research experts to present some of their latest approaches and future research directions in a language accessible to system theorists. Contributors discuss complex systems such as networks for modeling and control of civil structures, vehicles, robots, biomedical systems, fluid flow systems, and home automation systems. Each chapter provides theoretical and methodological descriptions of a specific application in the control of complex systems, including congestion control in computer networks, autonomous multi-robot docking systems, modeling and control in cancer genomics, and backstepping controllers for stabilization of turbulent flow PDEs. With this unique reference, you will discover how complexity is dealt with in different disciplines and learn about the latest methodologies, which are applicable to your own specialty. The balanced mix of theory and simulation presented by Modeling and Control of Complex Systems supplies a strong vehicle for enlarging your knowledge base a fueling future advances and incredible breakthroughs.

Neural Network Engineering in Dynamic Control Systems

Neural Network Engineering in Dynamic Control Systems
A Book

by Kenneth J. Hunt,George R. Irwin,Kevin Warwick

  • Publisher : Springer Science & Business Media
  • Release : 2012-12-06
  • Pages : 282
  • ISBN : 1447130669
  • Language : En, Es, Fr & De
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The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology impacts all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies, .... , new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series offers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. Within the control community there has been much discussion of and interest in the new Emerging Technologies and Methods. Neural networks along with Fuzzy Logic and Expert Systems is an emerging methodology which has the potential to contribute to the development of intelligent control technologies. This volume of some thirteen chapters edited by Kenneth Hunt, George Irwin and Kevin Warwick makes a useful contribution to the literature of neural network methods and applications. The chapters are arranged systematically progressing from theoretical foundations, through the training aspects of neural nets and concluding with four chapters of applications. The applications include problems as diverse as oven tempera ture control, and energy/load forecasting routines. We hope this interesting but balanced mix of material appeals to a wide range of readers from the theoretician to the industrial applications engineer.

Neural Network Models

Neural Network Models
Theory and Projects

by Philippe de Wilde

  • Publisher : Springer Science & Business Media
  • Release : 1997-05-30
  • Pages : 174
  • ISBN : 9783540761297
  • Language : En, Es, Fr & De
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Providing an in-depth treatment of neural network models, this volume explains and proves the main results in a clear and accessible way. It presents the essential principles of nonlinear dynamics as derived from neurobiology, and investigates the stability, convergence behaviour and capacity of networks.

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

1993 IEEE International Conference on Neural Networks, San Francisco, California, March 28-April 1, 1993

1993 IEEE International Conference on Neural Networks, San Francisco, California, March 28-April 1, 1993
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 1993
  • Pages : 1983
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Food Processing Operations Modeling

Food Processing Operations Modeling
Design and Analysis

by Joseph M. Irudayaraj

  • Publisher : CRC Press
  • Release : 2001-02-27
  • Pages : 368
  • ISBN : 9780203908105
  • Language : En, Es, Fr & De
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A comprehensive survey of thermal processing and modelling techniques in food process engineering. It combines theory and practice to solve actual problems in the food processing industry - emphasizing heat and mass transfer, fluid flow, electromagnetics, stochastic processes, and neural network analysis in food systems. There are specific case stu