This three-part monograph addresses topics in the areas of control systems, signal processing and neural networks. Procedures and results are determined which constitute the first successful synthesis procedure for associative memories by means of artificial neural networks with arbitrarily pre-specified full or partial interconnecting structure and with or without symmetry constraints for the connection matrix.

Dynamical Systems With Saturation Nonlinearities: Analysis and Design (Lecture Notes in Control & Information Sciences)

Science & Math

Michel, Anthony N.

Liu, Derong

Mathematics

Springer, Verlag

Fuzzy logic methodology has proven effective in dealing with complex nonlinear systems containing uncertainties that are otherwise difficult to model. Technology based on this methodology is applicable to many real-world problems, especially in the area of consumer products. This book presents the first comprehensive, unified treatment of fuzzy modeling and fuzzy control, providing tools for the control of complex nonlinear systems. Coverage includes model complexity, model precision, and computing time. This is an excellent reference for electrical, computer, chemical, industrial, civil, manufacturing, mechanical and aeronautical engineers, and also useful for graduate courses in electrical engineering, computer engineering, and computer science.

Fuzzy Modeling and Fuzzy Control (Control Engineering)

Zhang, Huaguang

Computers & Technology

Birkhauser

"Analyzes the behavior, design, and implementation of artificial recurrent neural networks. Offers methods of synthesis for associative memories. Evaluates the qualitative properties and limitations of neural networks. Contains practical applications for optimal system performance."

Qualitative Analysis and Synthesis of Recurrent Neural Networks (Chapman & Hall/CRC Pure and Applied Mathematics)

Michel, Anthony

Certification

CRC-Press

Provides an in-depth and even treatment of the three pillars of computational intelligence and how they relate to one another This book covers the three fundamental topics that form the basis of computational intelligence: neural networks, fuzzy systems, and evolutionary computation. The text focuses on inspiration, design, theory, and practical aspects of implementing procedures to solve real-world problems. While other books in the three fields that comprise computational intelligence are written by specialists in one discipline, this book is co-written by current former Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems, a former Editor-in-Chief of IEEE Transactions on Fuzzy Systems, and the founding Editor-in-Chief of IEEE Transactions on Evolutionary Computation. The coverage across the three topics is both uniform and consistent in style and notation. <ul>
<li>Discusses single-layer and multilayer neural networks, radial-basis function networks, and recurrent neural networks</li> <li>Covers fuzzy set theory, fuzzy relations, fuzzy logic interference, fuzzy clustering and classification, fuzzy measures and fuzzy integrals</li> <li>Examines evolutionary optimization, evolutionary learning and problem solving, and collective intelligence</li> <li>Includes end-of-chapter practice problems that will help readers apply methods and techniques to real-world problems</li> </ul>
Fundamentals of Computational intelligence is written for advanced undergraduates, graduate students, and practitioners in electrical and computer engineering, computer science, and other engineering disciplines.

Fundamentals of Computational Intelligence: Neural Networks, Fuzzy Systems, and Evolutionary Computation (IEEE Press Series on Computational Intelligence)

Engineering & Transportation

Fogel, David B.

Keller, James M.

Engineering

Wiley-IEEE Press

Controlling Chaos: Suppression, Synchronization and Chaotification (Communications and Control Engineering)

This text offers a wide selection of techniques aimed at achieving three goals: the suppression, the synchronization, and the generation of chaos. It includes introductory material reviewing the history of chaos control and key mathematical preliminaries.

Wang, Zhiliang

Reference

Springer

There are many methods of stable controller design for nonlinear systems. In seeking to go beyond the minimum requirement of stability, Adaptive Dynamic Programming in Discrete Time approaches the challenging topic of optimal control for nonlinear systems using the tools of adaptive dynamic programming (ADP). The range of systems treated is extensive; affine, switched, singularly perturbed and time-delay nonlinear systems are discussed as are the uses of neural networks and techniques of value and policy iteration. The text features three main aspects of ADP in which the methods proposed for stabilization and for tracking and games benefit from the incorporation of optimal control methods: • infinite-horizon control for which the difficulty of solving partial differential Hamilton–Jacobi–Bellman equations directly is overcome, and proof provided that the iterative value function updating sequence converges to the infimum of all the value functions obtained by admissible control law sequences; • finite-horizon control, implemented in discrete-time nonlinear systems showing the reader how to obtain suboptimal control solutions within a fixed number of control steps and with results more easily applied in real systems than those usually gained from infinite-horizon control; • nonlinear games for which a pair of mixed optimal policies are derived for solving games both when the saddle point does not exist, and, when it does, avoiding the existence conditions of the saddle point. Non-zero-sum games are studied in the context of a single network scheme in which policies are obtained guaranteeing system stability and minimizing the individual performance function yielding a Nash equilibrium. In order to make the coverage suitable for the student as well as for the expert reader, Adaptive Dynamic Programming in Discrete Time: • establishes the fundamental theory involved clearly with each chapter devoted to a clearly identifiable control paradigm; • demonstrates convergence proofs of the ADP algorithms to deepen understanding of the derivation of stability and convergence with the iterative computational methods used; and • shows how ADP methods can be put to use both in simulation and in real applications. This text will be of considerable interest to researchers interested in optimal control and its applications in operations research, applied mathematics computational intelligence and engineering. Graduate students working in control and operations research will also find the ideas presented here to be a source of powerful methods for furthering their study.

Adaptive Dynamic Programming for Control: Algorithms and Stability (Communications and Control Engineering)

Wang, Ding

Luo, Yanhong

Controlling Chaos achieves three goals: the suppression, synchronisation and generation of chaos, each of which is the focus of a separate part of the book. The text deals with the well-known Lorenz, Rössler and Hénon attractors and the Chua circuit and with less celebrated novel systems. Modelling of chaos is accomplished using difference equations and ordinary and time-delayed differential equations. The methods directed at controlling chaos benefit from the influence of advanced nonlinear control theory: inverse optimal control is used for stabilization; exact linearization for synchronization; and impulsive control for chaotification. Notably, a fusion of chaos and fuzzy systems theories is employed. Time-delayed systems are also studied. The results presented are general for a broad class of chaotic systems. This monograph is self-contained with introductory material providing a review of the history of chaos control and the necessary mathematical preliminaries for working with dynamical systems.

The second edition of this textbook provides a single source for the analysis of system models represented by continuous-time and discrete-time, finite-dimensional and infinite-dimensional, and continuous and discontinuous dynamical systems. For these system models, it presents results which comprise the classical Lyapunov stability theory involving monotonic Lyapunov functions, as well as corresponding contemporary stability results involving non-monotonic Lyapunov functions. Specific examples from several diverse areas are given to demonstrate the applicability of the developed theory to many important classes of systems, including digital control systems, nonlinear regulator systems, pulse-width-modulated feedback control systems, and artificial neural networks.
 The authors cover the following four general topics:
 - Representation and modeling of dynamical systems of the types described above
- Presentation of Lyapunov and Lagrange stability theory for dynamical systems defined on general metric spaces involving monotonic and non-monotonic Lyapunov functions
- Specialization of this stability theory to finite-dimensional dynamical systems 
- Specialization of this stability theory to infinite-dimensional dynamical systems
Replete with examples and requiring only a basic knowledge of linear algebra, analysis, and differential equations, this book can be used as a textbook for graduate courses in stability theory of dynamical systems. It may also serve as a self-study reference for graduate students, researchers, and practitioners in applied mathematics, engineering, computer science, economics, and the physical and life sciences.
Review of the First Edition:
“The authors have done an excellent job maintaining the rigor of the presentation, and in providing standalone statements for diverse types of systems. [This] is a very interesting book which complements the existing literature. [It] is clearly written, and difficult concepts are illustrated by means of good examples.” 
- Alessandro Astolfi, IEEE Control Systems Magazine, February 2009

Stability of Dynamical Systems: On the Role of Monotonic and Non-Monotonic Lyapunov Functions (Systems & Control: Foundations & Applications)

Hou, Ling

The second edition of this textbook &nbsp;provides a single source for the analysis of system models represented by continuous-time and discrete-time, finite-dimensional and infinite-dimensional, and continuous and discontinuous dynamical systems.&nbsp; For these system models, it presents results which comprise the classical Lyapunov stability theory involving monotonic Lyapunov functions, as well as corresponding contemporary stability results involving non-monotonic Lyapunov functions.&nbsp; Specific examples from several diverse areas are given to demonstrate the applicability of the developed theory to many important classes of systems, including digital control systems, nonlinear regulator systems, pulse-width-modulated feedback control systems, and artificial neural networks. &nbsp;The authors cover the following four general topics: &nbsp;-&nbsp;Representation and modeling of dynamical systems of the types described above -&nbsp;Presentation of Lyapunov and Lagrange stability theory for dynamical systems defined on general metric spaces involving monotonic and non-monotonic Lyapunov functions -&nbsp;Specialization of this stability theory to finite-dimensional dynamical systems -&nbsp;Specialization of this stability theory to infinite-dimensional dynamical systems Replete with examples and requiring only a basic knowledge of linear algebra, analysis, and differential equations, this book can be used as a textbook for graduate courses in stability theory of dynamical systems.&nbsp; It may also serve as a self-study reference for graduate students, researchers, and practitioners in applied mathematics, engineering, computer science, economics, and the physical and life sciences. Review of the First Edition: "The authors have done an excellent job maintaining the rigor of the presentation, and in providing standalone statements for diverse types of systems.&nbsp; [This] is a very interesting book which complements the existing literature. [It] is clearly written, and difficult concepts are illustrated by means of good examples." - Alessandro Astolfi, IEEE Control Systems Magazine, February 2009

This book covers the most recent developments in adaptive dynamic programming (ADP). The text begins with a thorough background review of ADP making sure that readers are sufficiently familiar with the fundamentals. In the core of the book, the authors address first discrete- and then continuous-time systems. Coverage of discrete-time systems starts with a more general form of value iteration to demonstrate its convergence, optimality, and stability with complete and thorough theoretical analysis. A more realistic form of value iteration is studied where value function approximations are assumed to have finite errors. Adaptive Dynamic Programming also details another avenue of the ADP approach: policy iteration. Both basic and generalized forms of policy-iteration-based ADP are studied with complete and thorough theoretical analysis in terms of convergence, optimality, stability, and error bounds. Among continuous-time systems, the control of affine and nonaffine nonlinear systems is studied using the ADP approach which is then extended to other branches of control theory including decentralized control, robust and guaranteed cost control, and game theory. In the last part of the book the real-world significance of ADP theory is presented, focusing on three application examples developed from the authors’ work: • renewable energy scheduling for smart power grids;• coal gasification processes; and• water–gas shift reactions. Researchers studying intelligent control methods and practitioners looking to apply them in the chemical-process and power-supply industries will find much to interest them in this thorough treatment of an advanced approach to control.

Adaptive Dynamic Programming with Applications in Optimal Control (Advances in Industrial Control)

杨雄

Wei, Qinglai

Li, Hongliang

Computer Science

See the best price to sell, buy, or rent books by Liu, Derong. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Liu, Derong

See the best price to sell, buy, or rent books by Liu, Derong. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Dynamical Systems With Saturation Nonlinearities: Analysis and Design (Lecture Notes in Control & Information Sciences)

Fuzzy Modeling and Fuzzy Control (Control Engineering)

Qualitative Analysis and Synthesis of Recurrent Neural Networks (Chapman & Hall/CRC Pure and Applied Mathematics)

Fundamentals of Computational Intelligence: Neural Networks, Fuzzy Systems, and Evolutionary Computation (IEEE Press Series on Computational Intelligence)

Controlling Chaos: Suppression, Synchronization and Chaotification (Communications and Control Engineering)

Adaptive Dynamic Programming for Control: Algorithms and Stability (Communications and Control Engineering)

Controlling Chaos: Suppression, Synchronization and Chaotification (Communications and Control Engineering)

Stability of Dynamical Systems: On the Role of Monotonic and Non-Monotonic Lyapunov Functions (Systems & Control: Foundations & Applications)

Stability of Dynamical Systems: On the Role of Monotonic and Non-Monotonic Lyapunov Functions (Systems & Control: Foundations & Applications)

Adaptive Dynamic Programming with Applications in Optimal Control (Advances in Industrial Control)