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Evolutionary Learning Algorithms for Neural Adaptive Control

Author: Dimitris C. Dracopoulos

Publisher: Springer

Published: 2013-12-21

Total Pages: 214

ISBN-13: 1447109031

Evolutionary Learning Algorithms for Neural Adaptive Control is an advanced textbook, which investigates how neural networks and genetic algorithms can be applied to difficult adaptive control problems which conventional results are either unable to solve , or for which they can not provide satisfactory results. It focuses on the principles involved, rather than on the modelling of the applications themselves, and therefore provides the reader with a good introduction to the fundamental issues involved.

Evolutionary Learning Algorithms for Neural Adaptive Control

Author: Dimitris Dracopoulos

Publisher:

Published: 2014-09-01

Total Pages: 224

ISBN-13: 9781447109044

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Learning Algorithms

Author: P. Mars

Publisher: CRC Press

Published: 2018-01-18

Total Pages: 231

ISBN-13: 1351090879

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Over the past decade, interest in computational or non-symbolic artificial intelligence has grown. The algorithms involved have the ability to learn from past experience, and therefore have significant potential in the adaptive control of signals and systems. This book focuses on the theory and applications of learning algorithms-stochastic learning automata; artificial neural networks; and genetic algorithms, evolutionary strategies, and evolutionary programming. Hybrid combinations of various algorithms are also discussed.Chapter 1 provides a brief overview of the topics discussed and organization of the text. The first half of the book (Chapters 2 through 4) discusses the basic theory of the learning algorithms, with one chapter devoted to each type. In the second half (Chapters 5 through 7), the emphasis is on a wide range of applications drawn from adaptive signal processing, system identification, and adaptive control problems in telecommunication networks.Learning Algorithms: Theory and Applications in Signal Processing, Control and Communications is an excellent text for final year undergraduate and first year graduate students in engineering, computer science, and related areas. Professional engineers and everyone involved in the application of learning techniques in adaptive signal processing, control, and communications will find this text a valuable synthesis of theory and practical application of the most useful algorithms.

Intelligent Adaptive Control

Author: Lakhmi C. Jain

Publisher: CRC Press

Published: 1998-12-29

Total Pages: 440

ISBN-13: 9780849398056

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This book describes important techniques, developments, and applications of computational intelligence in system control. Chapters present: an introduction to the fundamentals of neural networks, fuzzy logic, and evolutionary computing a rigorous treatment of intelligent control industrial applications of intelligent control and soft computing, including transportation, petroleum, motor drive, industrial automation, and fish processing other knowledge-based techniques, including vehicle driving aid and air traffic management Intelligent Adaptive Control provides a state-of-the-art treatment of practical applications of computational intelligence in system control. The book cohesively covers introductory and advanced theory, design, implementation, and industrial use - serving as a singular resource for the theory and application of intelligent control, particularly employing fuzzy logic, neural networks, and evolutionary computing.

Machine Learning Control – Taming Nonlinear Dynamics and Turbulence

Author: Thomas Duriez

Publisher: Springer

Published: 2016-11-02

Total Pages: 211

ISBN-13: 3319406248

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This is the first textbook on a generally applicable control strategy for turbulence and other complex nonlinear systems. The approach of the book employs powerful methods of machine learning for optimal nonlinear control laws. This machine learning control (MLC) is motivated and detailed in Chapters 1 and 2. In Chapter 3, methods of linear control theory are reviewed. In Chapter 4, MLC is shown to reproduce known optimal control laws for linear dynamics (LQR, LQG). In Chapter 5, MLC detects and exploits a strongly nonlinear actuation mechanism of a low-dimensional dynamical system when linear control methods are shown to fail. Experimental control demonstrations from a laminar shear-layer to turbulent boundary-layers are reviewed in Chapter 6, followed by general good practices for experiments in Chapter 7. The book concludes with an outlook on the vast future applications of MLC in Chapter 8. Matlab codes are provided for easy reproducibility of the presented results. The book includes interviews with leading researchers in turbulence control (S. Bagheri, B. Batten, M. Glauser, D. Williams) and machine learning (M. Schoenauer) for a broader perspective. All chapters have exercises and supplemental videos will be available through YouTube.

Intelligent Control

Author: Fouad Sabry

Publisher: One Billion Knowledgeable

Published: 2023-07-03

Total Pages: 164

ISBN-13:

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What Is Intelligent Control The term "intelligent control" refers to a category of control methods that make use of a number of different artificial intelligence computing methodologies, including neural networks, Bayesian probability, fuzzy logic, machine learning, reinforcement learning, evolutionary computation, and genetic algorithms. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Intelligent Control Chapter 2: Artificial Intelligence Chapter 3: Machine Learning Chapter 4: Reinforcement Learning Chapter 5: Neural Network Chapter 6: Adaptive Control Chapter 7: Computational Intelligence Chapter 8: Outline of Artificial Intelligence Chapter 9: Machine Learning Control Chapter 10: Data-driven Model (II) Answering the public top questions about intelligent control. (III) Real world examples for the usage of intelligent control in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of intelligent control' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of intelligent control.

Learning Algorithms

Author: Phil Mars

Publisher: CRC Press

Published: 1996-10-15

Total Pages: 240

ISBN-13: 9780849378966

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Over the past decade, interest in computational or non-symbolic artificial intelligence has grown. The algorithms involved have the ability to learn from past experience, and therefore have significant potential in the adaptive control of signals and systems. This book focuses on the theory and applications of learning algorithms-stochastic learning automata; artificial neural networks; and genetic algorithms, evolutionary strategies, and evolutionary programming. Hybrid combinations of various algorithms are also discussed. Chapter 1 provides a brief overview of the topics discussed and organization of the text. The first half of the book (Chapters 2 through 4) discusses the basic theory of the learning algorithms, with one chapter devoted to each type. In the second half (Chapters 5 through 7), the emphasis is on a wide range of applications drawn from adaptive signal processing, system identification, and adaptive control problems in telecommunication networks. Learning Algorithms: Theory and Applications in Signal Processing, Control and Communications is an excellent text for final year undergraduate and first year graduate students in engineering, computer science, and related areas. Professional engineers and everyone involved in the application of learning techniques in adaptive signal processing, control, and communications will find this text a valuable synthesis of theory and practical application of the most useful algorithms.

Genetic Programming III

Author: John R. Koza

Publisher: Morgan Kaufmann

Published: 1999

Total Pages: 1516

ISBN-13: 9781558605435

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Genetic programming (GP) is a method for getting a computer to solve a problem by telling it what needs to be done instead of how to do it. Koza, Bennett, Andre, and Keane present genetically evolved solutions to dozens of problems of design, control, classification, system identification, and computational molecular biology. Among the solutions are 14 results competitive with human-produced results, including 10 rediscoveries of previously patented inventions.

Application of Neural Networks to Adaptive Control of Nonlinear Systems

Author: Gee Wah Ng

Publisher:

Published: 1997

Total Pages: 0

ISBN-13: 9780863802140

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This book investigates the ability of a neural network (NN) to learn how to control an unknown (nonlinear, in general) system, using data acquired on-line, that is during the process of attempting to exert control. Two algorithms are developed to train the neural network for real-time control applications. The first algorithm is known as Learning by Recursive Least Squares (LRLS) algorithm and the second algorithm is known as Integrated Gradient and Least Squares (IGLS) algorithm. The ability of these algorithms to train the NN controller for real-time control is demonstrated on practical applications and the local convergence and stability requirements of these algorithms are analysed. In addition, network topology, learning algorithms (particularly supervised learning) and neural network control strategies are presented.

Adaptive Systems in Drug Design

Author: Gisbert Schneider

Publisher: CRC Press

Published: 2002-10-01

Total Pages: 169

ISBN-13: 149871370X

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A brief history of drug design presented to make clear that there are fashions in this important field and that they change rather rapidly. This is due in part to the fact that the way that a new paradigm is accepted in a drug company often does not depend on its scientific merit alone.