This original and timely monograph describes a unique self-contained excursion that reveals to the readers the roles of two basic cognitive abilities, i.e. intention recognition and arranging commitments, in the evolution of cooperative behavior. This book analyses intention recognition, an important ability that helps agents predict others' behavior, in its artificial intelligence and evolutionary computational modeling aspects, and proposes a novel intention recognition method. Furthermore, the book presents a new framework for intention-based decision making and illustrates several ways in which an ability to recognize intentions of others can enhance a decision making process. By employing the new intention recognition method and the tools of evolutionary game theory, this book introduces computational models demonstrating that intention recognition promotes the emergence of cooperation within populations of self-regarding agents. Finally, the book describes how commitment provides a pathway to the evolution of cooperative behavior, and how it further empowers intention recognition, thereby leading to a combined improved strategy.

This book introduces a new set of orthogonal hybrid functions (HF) which approximates time functions in a piecewise linear manner which is very suitable for practical applications. The book presents an analysis of different systems namely, time-invariant system, time-varying system, multi-delay systems---both homogeneous and non-homogeneous type- and the solutions are obtained in the form of discrete samples. The book also investigates system identification problems for many of the above systems. The book is spread over 15 chapters and contains 180 black and white figures, 18 colour figures, 85 tables and 56 illustrative examples. MATLAB codes for many such examples are included at the end of the book.

This book contains the proceedings of the Sixth National Conference of the Italian Systems Society. The title, Towards a post-Bertalanffy Systemics, aims to underline the need for Systemics and Systems Science to generalize theoretically concepts related to complexity (the great enemy of Bertalanffy Systemics). Hopefully this goal should be achieved by working in an inter-disciplinary and trans-disciplinary fashion, using systemic concepts arising from various disciplines and from the original, or Bertalanffy Systemics, as well. The interdisciplinary nature of the original Systemics and its power of generalization were given, overall, by the fact that the problems and solutions of one discipline become problems and solutions for another. Today, the modeling and interpretation of multidisciplinary approaches and representations makes easier to recognize these interconnections. The context, however, has changed dramatically. Of course, the challenge is still to find theoretical generalizations and applications, even where we have a lot of specificities, but we know very little on how to combine them. We cannot, however, simply replace the old with the new, but we must introduce strategies to recognize, represent, model and act on new levels, combining multiple representations, functions and emergence. In many disciplines this has been already done, and inevitably well, since targets and projects are well specified and oriented. The challenge is to do it for Systemics, with the vocations of cultural and theoretical generalization. Examples of new issues introduced by such theoretical disciplinary improvements, dealt with by many disciplines, include the study of mesoscopic or middle-way level, of multiple and dynamic coherence, of equivalence/non-equivalence, of fractality, of networks, of non-causality, of non-invasiveness, of non-prescribability, of non-separability, of quasi properties, of symmetry properties, of topological dynamics, as well as of quantum theories and concepts. The conference was devoted to identifying, discussing and understanding possible interrelationships of theoretical disciplinary improvements, recognized as having prospective fundamental roles for a new post-Bertalanffy Systemics. The latter should be able to deal with problems related to complexity in a generalized way. In this context the inter-disciplinarity should consists, for instance, in a disciplinary reformulation of problems, as from algebraic to geometrical, from military to political, from biological to chemical, while the trans-disciplinarity should be related to the study of such reformulations and their properties. The Italian Systems Society (AIRS) was founded in the 1996. The AIRS is a network of academicians, scientists, researchers and professionals involved in Systemics. A partial list of disciplines represented is: Architecture Biology Economics Education Engineering Mathematics Neurosciences Medicine Music Philosophy Psychology Physics. Previous conferences had as open lecturers professors Arecchi, Haken, Klir, and Kauffman. The proceedings have been published as: 1. Minati, G., (ed.), (1998), Proceedings of the first Italian Conference on Systemics, Apogeo Scientifica, Milan, Italy. 2. Minati, G., and Pessa, E., (eds.) (2002), Emergence in Complex Cognitive, Social and Biological Systems. Kluwer, New York. 3. Minati, G., Pessa, E., and Abram, M., (eds.), (2006), Systemics of Emergence: Research and Applications. Springer, New York. 4. Minati, G., Abram, M. and Pessa, E., (eds.), (2009), Processes of emergence of systems and systemic properties. Towards a general theory of emergence. World Scientific, Singapore. 5. Minati, G., Abram, M. and Pessa, E., (eds.), (2012), Methods, Models, simulations and approaches - towards a general theory of change. World Scientific, Singapore.

This self-contained text develops a Markov chain approach that makes the rigorous analysis of a class of microscopic models that specify the dynamics of complex systems at the individual level possible. It presents a general framework of aggregation in agent-based and related computational models, one which makes use of lumpability and information theory in order to link the micro and macro levels of observation. The starting point is a microscopic Markov chain description of the dynamical process in complete correspondence with the dynamical behavior of the agent-based model (ABM), which is obtained by considering the set of all possible agent configurations as the state space of a huge Markov chain. An explicit formal representation of a resulting "micro-chain" including microscopic transition rates is derived for a class of models by using the random mapping representation of a Markov process. The type of probability distribution used to implement the stochastic part of the model, which defines the updating rule and governs the dynamics at a Markovian level, plays a crucial part in the analysis of "voter-like" models used in population genetics, evolutionary game theory and social dynamics. The book demonstrates that the problem of aggregation in ABMs - and the lumpability conditions in particular - can be embedded into a more general framework that employs information theory in order to identify different levels and relevant scales in complex dynamical systems

The book investigates stability theory in terms of two different measure, exhibiting the advantage of employing families of Lyapunov functions and treats the theory of a variety of inequalities, clearly bringing out the underlying theme. It also demonstrates manifestations of the general Lyapunov method, showing how this technique can be adapted to various apparently diverse nonlinear problems. Furthermore it discusses the application of theoretical results to several different models chosen from real world phenomena, furnishing data that is particularly relevant for practitioners. Stability Analysis of Nonlinear Systems is an invaluable single-sourse reference for industrial and applied mathematicians, statisticians, engineers, researchers in the applied sciences, and graduate students studying differential equations.

This proceedings volume contains talks and poster presentations from the International Symposium "Self-Organization in Complex Systems: The Past, Present, and Future of Synergetics", which took place at Hanse-Wissenschaftskolleg, an Institute of Advanced Studies, in Delmenhorst, Germany, during the period November 13 - 16, 2012. The Symposium was organized in honour of Hermann Haken, who celebrated his 85th birthday in 2012. With his fundamental theory of Synergetics he had laid the mathematical-physical basis for describing and analyzing self-organization processes in a diversity of fields of research. The quest for common and universal principles of self-organization in complex systems was clearly covered by the wide range of interdisciplinary topics reported during the Symposium. These extended from complexity in classical systems and quantum systems over self-organisation in neuroscience even to the physics of finance. Moreover, by combining a historical view with a present status report the Symposium conveyed an impression of the allure and potency of this branch of research as well as its applicability in the future.

This book presents the details of the BONUS algorithm and its real world applications in areas like sensor placement in large scale drinking water networks, sensor placement in advanced power systems, water management in power systems, and capacity expansion of energy systems. A generalized method for stochastic nonlinear programming based on a sampling based approach for uncertainty analysis and statistical reweighting to obtain probability information is demonstrated in this book. Stochastic optimization problems are difficult to solve since they involve dealing with optimization and uncertainty loops. There are two fundamental approaches used to solve such problems. The first being the decomposition techniques and the second method identifies problem specific structures and transforms the problem into a deterministic nonlinear programming problem. These techniques have significant limitations on either the objective function type or the underlying distributions for the uncertain variables. Moreover, these methods assume that there are a small number of scenarios to be evaluated for calculation of the probabilistic objective function and constraints. This book begins to tackle these issues by describing a generalized method for stochastic nonlinear programming problems. This title is best suited for practitioners, researchers and students in engineering, operations research, and management science who desire a complete understanding of the BONUS algorithm and its applications to the real world.

This book offers an overview of models, measurements, calculations and examples connecting musical acoustics and music psychology. Indeed, many mathematical formulations that explain musical acoustics can also be used to help predict human auditory perception.

In this monograph the authors present Newton-type, Newton-like and other numerical methods, which involve fractional derivatives and fractional integral operators, for the first time studied in the literature. All for the purpose to solve numerically equations whose associated functions can be also non-differentiable in the ordinary sense. That is among others extending the classical Newton method theory which requires usual differentiability of function. Chapters are self-contained and can be read independently and several advanced courses can be taught out of this book. An extensive list of references is given per chapter. The book's results are expected to find applications in many areas of applied mathematics, stochastics, computer science and engineering. As such this monograph is suitable for researchers, graduate students, and seminars of the above subjects, also to be in all science and engineering libraries.

This book is a concise navigator across the history of cybernetics, its state-of-the-art and prospects. The evolution of cybernetics (from N. Wiener to the present day) and the reasons of its ups and downs are presented. The correlation of cybernetics with the philosophy and methodology of control, as well as with system theory and systems analysis is clearly demonstrated. The book presents a detailed analysis focusing on the modern trends of research in cybernetics. A new development stage of cybernetics (the so-called cybernetics 2.0) is discussed as a science on general regularities of systems organization and control. The author substantiates the topicality of elaborating a new branch of cybernetics, i.e. organization theory which studies an organization as a property, process and system. The book is intended for theoreticians and practitioners, as well as for students, postgraduates and doctoral candidates. In the first place, the target audience includes tutors and lecturers preparing courses on cybernetics, control theory and systems science.

This book explores event-based estimation problems. It shows how several stochastic approaches are developed to maintain estimation performance when sensors perform their updates at slower rates only when needed. The self-contained presentation makes this book suitable for readers with no more than a basic knowledge of probability analysis, matrix algebra and linear systems. The introduction and literature review provide information, while the main content deals with estimation problems from four distinct angles in a stochastic setting, using numerous illustrative examples and comparisons. The text elucidates both theoretical developments and their applications, and is rounded out by a review of open problems. This book is a valuable resource for researchers and students who wish to expand their knowledge and work in the area of event-triggered systems. At the same time, engineers and practitioners in industrial process control will benefit from the event-triggering technique that reduces communication costs and improves energy efficiency in wireless automation applications.

This book presents recent advances, new ideas and novel techniques related to the field of nonlinear dynamics, including localized pattern formation, self-organization and chaos. Various natural systems ranging from nonlinear optics to mechanics, fluids and magnetic are considered. The aim of this book is to gather specialists from these various fields of research to promote cross-fertilization and transfer of knowledge between these active research areas. In particular, nonlinear optics and laser physics constitute an important part in this issue due to the potential applications for all-optical control of light, optical storage, and information processing. Other possible applications include the generation of ultra-short pulses using all-fiber cavities.

This research aims to achieve a fundamental understanding of synchronization and its interplay with the topology of complex networks. Synchronization is a ubiquitous phenomenon observed in different contexts in physics, chemistry, biology, medicine and engineering. Most prominently, synchronization takes place in the brain, where it is associated with several cognitive capacities but is - in abundance - a characteristic of neurological diseases. Besides zero-lag synchrony, group and cluster states are considered, enabling a description and study of complex synchronization patterns within the presented theory. Adaptive control methods are developed, which allow the control of synchronization in scenarios where parameters drift or are unknown. These methods are, therefore, of particular interest for experimental setups or technological applications. The theoretical framework is demonstrated on generic models, coupled chemical oscillators and several detailed examples of neural networks.

This book contains all refereed papers that were accepted to the sixth edition of the " Complex Systems Design & Management Paris " (CSD&M Paris 2015) international conference which took place in Paris (France) on November 23-25, 2015.These proceedings cover the most recent trends in the emerging field of complex systems sciences & practices from an industrial and academic perspective, including the main industrial domains (aeronautics & aerospace, defense & security, electronics & robotics, energy & environment, health & welfare, software & e-services, transportation), scientific & technical topics (systems fundamentals, systems architecture & engineering, systems metrics & quality, systems modeling tools) and systems types (artificial ecosystems, embedded systems, software & information systems, systems of systems, transportation systems).The CSD&M Paris 2015 conference is organized under the guidance of the CESAMES non-profit organization, address: CESAMES, 8 rue de Hanovre, 75002 Paris, France.

This contributed volume collects research papers, presented at the CIRP Sponsored Conference Robust Manufacturing Control: Innovative and Interdisciplinary Approaches for Global Networks (RoMaC 2012, Jacobs University, Bremen, Germany, June 18th-20th 2012). These research papers present the latest developments and new ideas focusing on robust manufacturing control for global networks. Today, Global Production Networks (i.e. the nexus of interconnected material and information flows through which products and services are manufactured, assembled and distributed) are confronted with and expected to adapt to: sudden and unpredictable large-scale changes of important parameters which are occurring more and more frequently, event propagation in networks with high degree of interconnectivity which leads to unforeseen fluctuations, and non-equilibrium states which increasingly characterize daily business. These multi-scale changes deeply influence logistic target achievement and call for robust planning and control strategies. Therefore, understanding the cause and effects of multi-scale changes in production networks is of major interest. New methodological approaches from different science disciplines are promising to contribute to a new level comprehension of network processes. Unconventional methods from biology, perturbation ecology or auditory display are gaining increasing importance as they are confronted with similar challenges. Advancements from the classical disciplines such as mathematics, physics and engineering are also becoming of continuing importance.

It has been widely recognized nowadays the importance of introducing mathematical models that take into account possible sudden changes in the dynamical behavior of a high-integrity systems or a safety-critical system. Such systems can be found in aircraft control, nuclear power stations, robotic manipulator systems, integrated communication networks and large-scale flexible structures for space stations, and are inherently vulnerable to abrupt changes in their structures caused by component or interconnection failures. In this regard, a particularly interesting class of models is the so-called Markov jump linear systems (MJLS), which have been used in numerous applications including robotics, economics and wireless communication. Combining probability and operator theory, the present volume provides a unified and rigorous treatment of recent results in control theory of continuous-time MJLS. This unique approach is of great interest to experts working in the field of linear systems with Markovian jump parameters or in stochastic control. The volume focuses on one of the few cases of stochastic control problems with an actual explicit solution and offers material well-suited to coursework, introducing students to an interesting and active research area. The book is addressed to researchers working in control and signal processing engineering. Prerequisites include a solid background in classical linear control theory, basic familiarity with continuous-time Markov chains and probability theory, and some elementary knowledge of operator theory.

With so many terms available to define the same thing, it would seem nearly irresponsible to introduce yet another term (complex situation) to describe a phenomenological state of such as a system. However, a complex situation infers both a broader meaning and imposes a different perspective. Complex in this context is dependent on understanding and reality rather than observer and knowledge. Situation imposes a gestalt that cannot be characterized within a singular perspective that relegates paradox to a superior/subordinate hierarchy. This also infers that complex situation has no monotonic definition or each definition is by default incomplete. Therefore the perennial derivations for systems such as complex systems, system of systems, federation of systems is no longer a sufficient descriptor for complex situation. Ergo system and its genealogy lack the constitution to define complex situations. The books' intent is to explore this pathology through a series of papers written by authors that work in complex situations and have dealt with the limitations of the status quo: systems.

Markov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, computer science, and the social sciences. Many real-world problems modeled by MDPs have huge state and/or action spaces, giving an opening to the curse of dimensionality and so making practical solution of the resulting models intractable. In other cases, the system of interest is too complex to allow explicit specification of some of the MDP model parameters, but simulation samples are readily available (e.g., for random transitions and costs). For these settings, various sampling and population-based algorithms have been developed to overcome the difficulties of computing an optimal solution in terms of a policy and/or value function. Specific approaches include adaptive sampling, evolutionary policy iteration, evolutionary random policy search, and model reference adaptive search. This substantially enlarged new edition reflects the latest developments in novel algorithms and their underpinning theories, and presents an updated account of the topics that have emerged since the publication of the first edition. Includes: innovative material on MDPs, both in constrained settings and with uncertain transition properties; game-theoretic method for solving MDPs; theories for developing roll-out based algorithms; and details of approximation stochastic annealing, a population-based on-line simulation-based algorithm. The self-contained approach of this book will appeal not only to researchers in MDPs, stochastic modeling, and control, and simulation but will be a valuable source of tuition and reference for students of control and operations research.

This intriguing book was born out of the many discussions the authors had in the past 10 years about the role of scale-free structure and dynamics in producing intelligent behavior in brains. The microscopic dynamics of neural networks is well described by the prevailing paradigm based in a narrow interpretation of the neuron doctrine. This book broadens the doctrine by incorporating the dynamics of neural fields, as first revealed by modeling with differential equations (K-sets). The book broadens that approach by application of random graph theory (neuropercolation). The book concludes with diverse commentaries that exemplify the wide range of mathematical/conceptual approaches to neural fields. This book is intended for researchers, postdocs, and graduate students, who see the limitations of network theory and seek a beachhead from which to embark on mesoscopic and macroscopic neurodynamics.

This monograph presents a simple and efficient two-relay control algorithm for generation of self-excited oscillations of a desired amplitude and frequency in dynamic systems. Developed by the authors, the two-relay controller consists of two relays switched by the feedback received from a linear or nonlinear system, and represents a new approach to the self-generation of periodic motions in underactuated mechanical systems. The first part of the book explains the design procedures for two-relay control using three different methodologies - the describing-function method, Poincare maps, and the locus-of-a perturbed-relay-system method - and concludes with stability analysis of designed periodic oscillations. Two methods to ensure the robustness of two-relay control algorithms are explored in the second part, one based on the combination of the high-order sliding mode controller and backstepping, and the other on higher-order sliding-modes-based reconstruction of uncertainties and their compensation where Lyapunov-based stability analysis of tracking error is used. Finally, the third part illustrates applications of self-oscillation generation by a two-relay control with a Furuta pendulum, wheel pendulum, 3-DOF underactuated robot, 3-DOF laboratory helicopter, and fixed-phase electronic circuits. Self-Oscillations in Dynamic Systems will appeal to engineers, researchers, and graduate students working on the tracking and self-generation of periodic motion of electromechanical systems, including non-minimum-phase systems. It will also be of interest to mathematicians working on analysis of periodic solutions.

Functional analysis owes much of its early impetus to problems that arise in the calculus of variations. In turn, the methods developed there have been applied to optimal control, an area that also requires new tools, such as nonsmooth analysis. This self-contained textbook gives a complete course on all these topics. It is written by a leading specialist who is also a noted expositor. This book provides a thorough introduction to functional analysis and includes many novel elements as well as the standard topics. A short course on nonsmooth analysis and geometry completes the first half of the book whilst the second half concerns the calculus of variations and optimal control. The author provides a comprehensive course on these subjects, from their inception through to the present. A notable feature is the inclusion of recent, unifying developments on regularity, multiplier rules, and the Pontryagin maximum principle, which appear here for the first time in a textbook. Other major themes include existence and Hamilton-Jacobi methods. The many substantial examples, and the more than three hundred exercises, treat such topics as viscosity solutions, nonsmooth Lagrangians, the logarithmic Sobolev inequality, periodic trajectories, and systems theory. They also touch lightly upon several fields of application: mechanics, economics, resources, finance, control engineering. Functional Analysis, Calculus of Variations and Optimal Control is intended to support several different courses at the first-year or second-year graduate level, on functional analysis, on the calculus of variations and optimal control, or on some combination. For this reason, it has been organized with customization in mind. The text also has considerable value as a reference. Besides its advanced results in the calculus of variations and optimal control, its polished presentation of certain other topics (for example convex analysis, measurable selections, metric regularity, and nonsmooth analysis) will be appreciated by researchers in these and related fields.

This book, edited and authored by a closely collaborating network of social scientists and psychologists, recasts typical research topics in these fields into the language of nonlinear, dynamic and complex systems. The aim is to provide scientists with different backgrounds - physics, applied mathematics and computer sciences - with the opportunity to apply the tools of their trade to an altogether new range of possible applications. At the same time, this book will serve as a first reference for a new generation of social scientists and psychologists wishing to familiarize themselves with the new methodology and the "thinking in complexity".

This volume features key contributions from the International Conference on Pattern Recognition Applications and Methods, (ICPRAM 2012,) held in Vilamoura, Algarve, Portugal from February 6th-8th, 2012. The conference provided a major point of collaboration between researchers, engineers and practitioners in the areas of Pattern Recognition, both from theoretical and applied perspectives, with a focus on mathematical methodologies. Contributions describe applications of pattern recognition techniques to real-world problems, interdisciplinary research, and experimental and theoretical studies which yield new insights that provide key advances in the field. This book will be suitable for scientists and researchers in optimization, numerical methods, computer science, statistics and for differential geometers and mathematical physicists.

Today it appears that we understand more about the universe than about our interconnected socio-economic world. In order to uncover organizational structures and novel features in these systems, we present the first comprehensive complex systems analysis of real-world ownership networks. This effort lies at the interface between the realms of economics and the emerging field loosely referred to as complexity science. The structure of global economic power is reflected in the network of ownership ties of companies and the analysis of such ownership networks has possible implications for market competition and financial stability. Thus this work presents powerful new tools for the study of economic and corporate networks that are only just beginning to attract the attention of scholars.