This book introduces readers to essential tools for the measurement and analysis of information loss in signal processing systems. Employing a new information-theoretic systems theory, the book analyzes various systems in the signal processing engineer's toolbox: polynomials, quantizers, rectifiers, linear filters with and without quantization effects, principal components analysis, multirate systems, etc. The user benefit of signal processing is further highlighted with the concept of relevant information loss. Signal or data processing operates on the physical representation of information so that users can easily access and extract that information. However, a fundamental theorem in information theory-data processing inequality-states that deterministic processing always involves information loss. These measures form the basis of a new information-theoretic systems theory, which complements the currently prevailing approaches based on second-order statistics, such as the mean-squared error or error energy. This theory not only provides a deeper understanding but also extends the design space for the applied engineer with a wide range of methods rooted in information theory, adding to existing methods based on energy or quadratic representations.

This contributed volume offers a collection of papers presented at the 2016 Network Games, Control, and Optimization conference (NETGCOOP), held at the University of Avignon in France, November 23-25, 2016. These papers highlight the increasing importance of network control and optimization in many networking application domains, such as mobile and fixed access networks, computer networks, social networks, transportation networks, and, more recently, electricity grids and biological networks. Covering a wide variety of both theoretical and applied topics in the areas listed above, the authors explore several conceptual and algorithmic tools that are needed for efficient and robust control operation, performance optimization, and better understanding the relationships between entities that may be acting cooperatively or selfishly in uncertain and possibly adversarial environments. As such, this volume will be of interest to applied mathematicians, computer scientists, engineers, and researchers in other related fields.

Complexity and dynamic order of controlled engineering systems is constantly increasing. Complex large scale systems (where "large" reflects the system's order and not necessarily its physical size) appear in many engineering fields, such as micro-electromechanics, manufacturing, aerospace, civil engineering and power engineering. Modeling of these systems often result in very high-order models imposing great challenges to the analysis, design and control problems. "Efficient Modeling and Control of Large-Scale Systems" compiles state-of-the-art contributions on recent analytical and computational methods for addressing model reduction, performance analysis and feedback control design for such systems. Also addressed at length are new theoretical developments, novel computational approaches and illustrative applications to various fields, along with: - An interdisciplinary focus emphasizing methods and approaches that can be commonly applied in various engineering fields -Examinations of applications in various fields including micro-electromechanical systems (MEMS), manufacturing processes, power networks, traffic control "Efficient Modeling and Control of Large-Scale Systems" is an ideal volume for engineers and researchers working in the fields of control and dynamic systems.

The present book is devoted to problems of adaptation of artificial neural networks to robust fault diagnosis schemes. It presents neural networks-based modelling and estimation techniques used for designing robust fault diagnosis schemes for non-linear dynamic systems.

A part of the book focuses on fundamental issues such as architectures of dynamic neural networks, methods for designing of neural networks and fault diagnosis schemes as well as the importance of robustness. The book is of a tutorial value and can be perceived as a good starting point for the new-comers to this field. The book is also devoted to advanced schemes of description of neural model uncertainty. In particular, the methods of computation of neural networks uncertainty with robust parameter estimation are presented. Moreover, a novel approach for system identification with the state-space GMDH neural network is delivered.

All the concepts described in this book are illustrated by both simple academic illustrative examples and practical applications.

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This book applies cutting-edge methods from cognitive and evolutionary theories to develop models of conflict between hierarchically-structured cognitive entities under circumstances of imprecision, uncertainty and stress. Characterized as friction and the fog-of-war by the Prussian military theorist Carl von Clausewitz, such conditions impair institutional cognition in real-time conflict and pose a real and continuing threat to organizations, such as the US military. In a linked collection of formal essays and a mathematical appendix, the book explores different aspects of cognitive and evolutionary process as conducted under the direction of doctrine that acts as a kind of genome for retention of what is learned through Lamarckian evolutionary selection pressures: armies and corporate entities learn from conflict, and incorporate that learning into their ongoing procedures. The book proposes models and policy solutions for strategic competence. A central feature of the book is a formal description of the famous OODA loop of the US military theorist John Boyd in terms of the Data Rate Theorem that links control and information theories. That description is expanded to cover more fully the impact of stochastic fog-of-war effects on tactical and operational scales of conflict. Subsequent chapters examine in more detail the role of doctrine, and the particular effect of embedding culture on cognitive and Lamarckian evolutionary processes associated with conflict on tactical, operational, and strategic scales and levels of organization. A scientifically sophisticated exercise in applied mathematics, history, evolutionary theory, and ecosystem theory, this book will be appropriate for researchers and students interested in defense, security, and international relations, as well as non-academic career professionals in government and industry.

Lattice-valued Logic aims at establishing the logical foundation for uncertain information processing routinely performed by humans and artificial intelligence systems. In this textbook for the first time a general introduction on lattice-valued logic is given. It systematically summarizes research from the basic notions up to recent results on lattice implication algebras, lattice-valued logic systems based on lattice implication algebras, as well as the corresponding reasoning theories and methods. The book provides the suitable theoretical logical background of lattice-valued logic systems and supports newly designed intelligent uncertain-information-processing systems and a wide spectrum of intelligent learning tasks.

The key attribute of a Fault Tolerant Control (FTC) system is its ability to maintain overall system stability and acceptable performance in the face of faults and failures within the feedback system. In this book Integral Sliding Mode (ISM) Control Allocation (CA) schemes for FTC are described, which have the potential to maintain close to nominal fault-free performance (for the entire system response), in the face of actuator faults and even complete failures of certain actuators. Broadly an ISM controller based around a model of the plant with the aim of creating a nonlinear fault tolerant feedback controller whose closed-loop performance is established during the design process. The second approach involves retro-fitting an ISM scheme to an existing feedback controller to introduce fault tolerance. This may be advantageous from an industrial perspective, because fault tolerance can be introduced without changing the existing control loops. A high fidelity benchmark model of a large transport aircraft is used to demonstrate the efficacy of the FTC schemes. In particular a scheme based on an LPV representation has been implemented and tested on a motion flight simulator.

This volume is devoted to presentation of new results of research on systems of non-integer order, called also fractional systems. Their analysis and practical implementation have been the object of spontaneous development for a few last decades. The fractional order models can depict a physical plant better than the classical integer order ones. This covers different research fields such as insulator properties, visco-elastic materials, electrodynamic, electrothermal, electrochemical, economic processes modelling etc. On the other hand fractional controllers often outperform their integer order counterparts. This volume contains new ideas and examples of implementation, theoretical and pure practical aspects of using a non-integer order calculus. It is divided into four parts covering: mathematical fundamentals, modeling and approximations, controllability, observability and stability problems and practical applications of fractional control systems. The first part expands the base of tools and methods of the mathematical basis for non-integer order calculus. Part two focuses on new methods and developments in process modeling and fractional derivatives approximations. In the third part a bunch of papers which raise problems of controllability, observability and stability of non-integer order systems is provided. Part four is devoted to presentation of different fractional order control applications. This book was created thanks to many experts in the field of fractional calculus: authors, anonymous referees whose comments allowed us to improve the final form of the papers and active and inspiring discussion of the participants of RRNR'2015, the 7th Conference on Non-Integer Order Calculus and Its Applications that was organized by the Faculty of Electrical Engineering, West Pomeranian University of Technology, Szczecin, Poland.

Asymptotic Combinatorial Coding Theory is devoted to the investigation of the combinatorial properties of transmission systems using discrete signals. The book presents results of interest to specialists in combinatorics seeking to apply combinatorial methods to problems of combinatorial coding theory. Asymptotic Combinatorial Coding Theory serves as an excellent reference for resarchers in discrete mathematics, combinatorics, and combinatorial coding theory, and may be used as a text for advanced courses on the subject.

Contents 11. 2. 2. Four Main Areas of Dispute 247 11. 2. 3. Summary . . . 248 11. 3. Making Sense of the Issues . . 248 11. 3. 1. Introduction . . . . 248 11. 3. 2. The Scientific Approach 248 11. 3. 3. Science and Matters of Society . 249 11. 3. 4. Summary . 251 11. 4. Tying It All Together . . . . 251 11. 4. 1. Introduction . . . . 251 11. 4. 2. A Unifying Framework 251 11. 4. 3. Critical Systems Thinking 253 11. 4. 4. Summary 254 11. 5. Conclusion 254 Questions . . . 255 REFERENCES . . . . . . . . . . . . . . . . . . . 257 INDEX . . . . . . . . . . . . . . . . . . . . . . 267 Chapter One SYSTEMS Origin and Evolution, Terms and Concepts 1. 1. INTRODUCTION We start this book with Theme A (see Figure P. I in the Preface), which aims to develop an essential and fundamental understanding of systems science. So, what is systems science? When asked to explain what systems science is all about, many systems scientists are confronted with a rather daunting task. The discipline tends to be presented and understood in a fragmented way and very few people hold an overview understanding of the subject matter, while also having sufficient in-depth competence in many and broad-ranging subject areas where the ideas are used. Indeed, it was precisely this difficulty that identified the need for a comprehensive well-documented account such as is presented here in Dealing with Complexity.

We present an improved and enlarged version of our book Nonlinear - namics of Chaotic and Stochastic Systems published by Springer in 2002. Basically, the new edition of the book corresponds to its ?rst version. While preparingthiseditionwemadesomeclari?cationsinseveralsectionsandalso corrected the misprints noticed in some formulas. Besides, three new sections have been added to Chapter 2. They are "Statistical Properties of Dynamical Chaos," "E?ects of Synchronization in Extended Self-Sustained Oscillatory Systems," and "Synchronization in Living Systems." The sections indicated re?ect the most interesting results obtained by the authors after publication of the ?rst edition. We hope that the new edition of the book will be of great interest for a widesectionofreaderswhoarealreadyspecialistsorthosewhoarebeginning research in the ?elds of nonlinear oscillation and wave theory, dynamical chaos, synchronization, and stochastic process theory. Saratov, Berlin, and St. Louis V.S. Anishchenko November 2006 A.B. Neiman T.E. Vadiavasova V.V. Astakhov L. Schimansky-Geier Preface to the First Edition Thisbookisdevotedtotheclassicalbackgroundandtocontemporaryresults on nonlinear dynamics of deterministic and stochastic systems. Considerable attentionisgiventothee?ectsofnoiseonvariousregimesofdynamicsystems with noise-induced order. On the one hand, there exists a rich literature of excellent books on n- linear dynamics and chaos; on the other hand, there are many marvelous monographs and textbooks on the statistical physics of far-from-equilibrium andstochasticprocesses.Thisbookisanattempttocombinetheapproachof nonlinear dynamics based on the deterministic evolution equations with the approach of statistical physics based on stochastic or kinetic equations. One of our main aims is to show the important role of noise in the organization and properties of dynamic regimes of nonlinear dissipative systems.

The book is the first book on complex matrix equations including the conjugate of unknown matrices. The study of these conjugate matrix equations is motivated by the investigations on stabilization and model reference tracking control for discrete-time antilinear systems, which are a particular kind of complex system with structure constraints. It proposes useful approaches to obtain iterative solutions or explicit solutions for several types of complex conjugate matrix equation. It observes that there are some significant differences between the real/complex matrix equations and the complex conjugate matrix equations. For example, the solvability of a real Sylvester matrix equation can be characterized by matrix similarity; however, the solvability of the con-Sylvester matrix equation in complex conjugate form is related to the concept of con-similarity. In addition, the new concept of conjugate product for complex polynomial matrices is also proposed in order to establish a unified approach for solving a type of complex matrix equation.

1. MOTIVATION In many physical situations, a plant model is often provided with a qualitative or quantitative measure of associated model uncertainties. On the one hand, the validity of the model is guaranteed only inside a frequency band, so that nearly nothing can be said about the behavior of the real plant at high frequencies. On the other hand, if the model is derived on the basis of physical equations, it can be parameterized as a function of a few physical parameters, which are usually not perfectly known in practice. This is e.g. the case in aeronautical systems: as an example, the ae- dynamic model of an airplane is derived from the flight mechanics eq- tions. When synthesizing the aircraft control law, it is then necessary to take into account uncertainties in the values of the stability derivatives, which correspond to the physical coefficients of the aerodynamic model. Moreover, this airplane model does not perfectly represent the be- vior of the real aircraft. As a simple example, the flight control system or the autopilot are usually synthesized just using the aerodynamic model, thus without accounting for the flexible mechanicalstructure: the c- responding dynamics are indeed considered as high frequency neglected 1 dynamics, with respect to the dynamics of the rigid model .

At?rstsight,thisbookisaboutfacerecognitioninthebrain.Itsmorelasting value, however,lies in the paradigmatic way in which this particular problem is treated. From the basic ideas that are worked out here in concrete detail, it is a naturaland simple next step to at leastimagine, if not realizein model form, much more generalstructures and processes,thus helping to bridge the still tremendous chasm between mind and brain. It is the purpose of this foreword to point out these generic traits. For centuries, thinking about the brain has been dominated by the most complexmechanisticdevicesofthetime,clockwork,communicatinghydraulic tubesor,today,thecomputer.Thecomputer,takenasincarnationoftheU- versal Turing Machine, can implement any conceivable process, so that also a functional brain can surely be simulated on it, an idea that, beginning in the ?fties of the last century, has been seducing scientists to create "art- cial intelligence" in the computer. As a result we now have an information technology that displays many functional capabilities formerly regarded as the exclusive domain of the mind. As fascinating as this is, doting on "int- ligent machines" is systematically diverting our attention awayfrom the true problems of understanding the working of the brain.

System Modelling and Optimization covers research issues within systems theory, optimization, modelling, and computing. It includes contributions to structural mechanics, integer programming, nonlinear programming, interior point methods, dynamical systems, stability analysis, stochastic optimization, bilevel optimization, and semidefinite programming. Several survey papers written by leading experts in their fields complement new developments in theory and applications. This book contains most of the invited papers and a few carefully selected submitted papers that were presented at the 19th IFIP TC7 Conference on System Modelling and Optimization, which was held in Cambridge, England, from July 12 to 16, 1999, and sponsored by the International Federation for Information Processing (IFIP).

This book presents the latest information on both the theory and applications of networks, especially from the fields of transportation and communication, economics and human knowledge handling. It demonstrates that networks are of broad interest and that networks analysis from different disciplines offer unifying insight. Special attention is paid to networks in the ever increasing integration of Europe. Another point of focus is upon combinatorial aspects and the interactive effects between different networks, often known as synergetics.

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 Chapter 3 Flexible Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . 61 3. 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 3. 2 Flexible Unipolar Sigmoid Functions . . . . . . . . . . . . . . . . . . . . . . . . . . 62 3. 3 Flexible Bipolar Sigmoid Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 3. 4 Learning Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 3. 4. 1 Generalized learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 3. 4. 2 Specialized learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 3. 5 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 3. 6 Combinations of Flexible Artificial Neural Network Topologies . . . . 79 3. 7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 Chapter 4 Self-Tuning PID Control 85 4. 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 4. 2 PID Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 4. 3 Flexible Neural Network as an Indirect Controller . . . . . . . . . . . . . . . 91 4. 4 Self-tunig PID Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 4. 5 Simulation Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 4. 5. 1 The Tank model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 4. 5. 2 Simulation study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 4. 5. 3 Simulation results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 4. 6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 Chapter 5 Self-Tuning Computed Torque Control: Part I 107 5. 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 5. 2 Manipulator Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 5. 3 Computed Torque Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 5. 4 Self-tunig Computed Torque Control . . . . . . . . . . . . . . . . . . . . . . . . . 111 5. 5 Simulation Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 5. 5. 1 Simultaneous learning of connection weights and SF para- ters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 5. 5. 2 Learning of the sigmoid function parameters . . . . . . . . . . . . . 123 Vll 5. 5. 3 Simultaneous learning of SF parameters and output gains 129 5. 6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 Chapter 6 Self-Tuning Computed Torque Control: Part II 137 6. 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 6. 2 Simplification of Flexible Neural Networks . . . . . . . . . . . . . . . . . . . . 138 6. 3 Simulation Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 6. 3. 1 Simultaneous learning of connection weights and sigmoid function parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Pierre-Francois Verhulst, with his seminal work using the logistic map to describe population growth and saturation, paved the way for the many applications of this tool in modern mathematics, physics, chemistry, biology, economics and sociology. Indeed nowadays the logistic map is considered a useful and paradigmatic showcase for the route leading to chaos. This volume gathers contributions from some of the leading specialists in the field to present a state-of-the art view of the many ramifications of the developments initiated by Verhulst over a century ago.

A predictive control algorithm uses a model of the controlled system to predict the system behavior for various input scenarios and determines the most appropriate inputs accordingly. Predictive controllers are suitable for a wide range of systems; therefore, their advantages are especially evident when dealing with relatively complex systems, such as nonlinear, constrained, hybrid, multivariate systems etc. However, designing a predictive control strategy for a complex system is generally a difficult task, because all relevant dynamical phenomena have to be considered. Establishing a suitable model of the system is an essential part of predictive control design. Classic modeling and identification approaches based on linear-systems theory are generally inappropriate for complex systems; hence, models that are able to appropriately consider complex dynamical properties have to be employed in a predictive control algorithm. This book first introduces some modeling frameworks, which can encompass the most frequently encountered complex dynamical phenomena and are practically applicable in the proposed predictive control approaches. Furthermore, unsupervised learning methods that can be used for complex-system identification are treated. Finally, several useful predictive control algorithms for complex systems are proposed and their particular advantages and drawbacks are discussed. The presented modeling, identification and control approaches are complemented by illustrative examples. The book is aimed towards researches and postgraduate students interested in modeling, identification and control, as well as towards control engineers needing practically usable advanced control methods for complex systems.

Within a conceptual framework that is developed in the two first chapters, the actual application of systems thinking is described across a broad field of cases representing research, teaching, decision support and construction. All cases are presented by experts who have actually been involved in the activities they describe. Thus, the broad selection of cases captures the great variation of systems thinking and how it is integrated into models and theories and solid knowledge pertaining to different substantive areas. At the same time all case study authors address the same set of questions that are developed in the conceptual chapters. This gives comparability across cases - chapters - and brings cohesion to the book. The focus on Sweden, an advanced country in systems thinking, reinforces the unitary context in which comparison can be made between a systems approach for better research (theory), better practice and better design and construction.

Most recent literature on systems thinking has a general (often philosophical) perspective, concerns computer systems or focuses on one highly specific problem. A special feature of this book is that, while it concerns a single nation, it simultaneously contains a broad overview of systems analysis in a number of important issue areas, for research as well as practice, against a penetrating background description of what systems analysis is, how it functions, what problems it represents and what results it may produce.

The book is intended for a broad readership and can be appreciated by experts on systems thinking and analysis as well as by students, teachers, researchers, planners and policy makers who want to learn more aboutthis topic. The book should be useful in university teaching in several disciplines.

The aim of this book is to advocate and promote network models of linguistic systems that are both based on thorough mathematical models and substantiated in terms of linguistics. In this way, the book contributes first steps towards establishing a statistical network theory as a theoretical basis of linguistic network analysis the boarder of the natural sciences and the humanities. This book addresses researchers who want to get familiar with theoretical developments, computational models and their empirical evaluation in the field of complex linguistic networks. It is intended to all those who are interested in statistical models of linguistic systems from the point of view of network research. This includes all relevant areas of linguistics ranging from phonological, morphological and lexical networks on the one hand and syntactic, semantic and pragmatic networks on the other. In this sense, the volume concerns readers from many disciplines such as physics, linguistics, computer science and information science. It may also be of interest for the upcoming area of systems biology with which the chapters collected here share the view on systems from the point of view of network analysis.

This book is devoted to a domain of highest industrial and scienti?c interest, the complexity. The complexity understanding and management will be a main source of e?ciency and prosperity for the next decades. Complex systems areassembliesof multiple subsystemsand arecharact- ized by emergent behavior that results by nonlinear interactions among the subsystems at multiple levels of organization. Evolvability that is the ability to evolve is the method to confront and surpass the successive boundaries of complexity. Evolvability is not biological but should be considered here in the sense that the corresponding systems have, at di?erent levels, charact- istics that are naturally associated to the living systems. The signi?cance of the complexity and the phenomena of emergence are highlighted in the ?rst chapterofthe book.Theimplicationofconcepts aslevelofreality, circularity and closure for evolvable systems is evaluated. The second chapter of the book exposes the methodology to analyze and manage complex systems. The polystochastic models, PSMs, are the cons- ered mathematical tools. PSMs characterize systems emerging when several stochastic processes occurring at di?erent conditioning levels, are capable to interact with each other, resulting in qualitatively new processes and s- tems. Innovative are the higher categories approach and the introduction of apartialdi?erentialmodelfor multiple levelsmodeling.This imposes making use of appropriate notions of time, space, probabilities and entropy. Categorytheoryistheformalismcapabletooutlinethegeneralframework, shared by the functional organization of biological organisms, of cognitive systems, by the operational structure of evolvable technologies and devices and after all by the scienti?c and engineering methods

This book reports on the latest findings concerning nonlinear control theory and applications. It presents novel work on several kinds of commonly encountered nonlinear time-delay systems, including those whose nonlinear terms satisfy high-order polynomial form or general nonlinear form, those with nonlinear input or a triangular structure, and so on. As such, the book will be of interest to university researchers, R&D engineers and graduate students in the fields of control theory and control engineering who wish to learn about the core principles, methods, algorithms, and applications of nonlinear time-delay systems.

This book shows how, by rather simple models, we can gain remarkable insights into the behavior of complex systems. It is devoted to the discussion of functional self-organization in large populations of interacting active elements. The possible forms of self-organization in such systems range from coherent collective motions in the physical coordinate space to the mutual synchronization of internal dynamics, the development of coherently operating groups, the rise of hierarchical structures, and the emergence of dynamical networks. Such processes play an important role in biological and social phenomena. The authors have chosen a series of models from physics, biochemistry, biology, sociology and economics, and will systematically discuss their general properties. The book addresses researchers and graduate students in a variety of disciplines, such as physics, chemistry, biology and the social sciences.