The future obviously matters to us. It is, after all, where we'll be spending the rest of our lives. We need some degree of foresight if we are to make effective plans for managing our affairs. Much that we would like to know in advance cannot be predicted. But a vast amount of successful prediction is nonetheless possible, especially in the context of applied sciences such as medicine, meteorology, and engineering. This book examines our prospects for finding out about the future in advance. It addresses questions such as why prediction is possible in some areas and not others; what sorts of methods and resources make successful prediction possible; and what obstacles limit the predictive venture.

Nicholas Rescher develops a general theory of prediction that encompasses its fundamental principles, methodology, and practice and gives an overview of its promises and problems. Predicting the future considers the anthropological and historical background of the predictive enterprise. It also examines the conceptual, epistemic, and ontological principles that set the stage for predictive efforts. In short, Rescher explores the basic features of the predictive situation and considers their broader implications in science, in philosophy, and in the management of our daily affairs.

This second edition comprehensively presents important tools of linear systems theory, including differential and difference equations, Laplace and Z transforms, and more.
Linear Systems Theory discusses:
Nonlinear and linear systems in the state space form and through the transfer function method
Stability, including marginal stability, asymptotical stability, global asymptotical stability, uniform stability, uniform exponential stability, and BIBO stability
Controllability
Observability
Canonical forms
System realizations and minimal realizations, including state space approach and transfer function realizations
System design
Kalman filters
Nonnegative systems
Adaptive control
Neural networks
The book focuses mainly on applications in electrical engineering, but it provides examples for most branches of engineering, economics, and social sciences.
What's New in the Second Edition?
Case studies drawn mainly from electrical and mechanical engineering applications, replacing many of the longer case studies
Expanded explanations of both linear and nonlinear systems as well as new problem sets at the end of each chapter
Illustrative examples in all the chapters
An introduction and analysis of new stability concepts
An expanded chapter on neural networks, analyzing advances that have occurred in that field since the first edition
Although more mainstream than its predecessor, this revision maintains the rigorous mathematical approach of the first edition, providing fast, efficient development of the material.
Linear Systems Theory enables its reader to develop his or her capabilities for modeling dynamic phenomena, examining their properties, and applying them to real-life situations.

This book presents a new methodology with reduced time impact to address the problem of analog integrated circuit (IC) yield estimation by means of Monte Carlo (MC) analysis, inside an optimization loop of a population-based algorithm. The low time impact on the overall optimization processes enables IC designers to perform yield optimization with the most accurate yield estimation method, MC simulations using foundry statistical device models considering local and global variations. The methodology described by the authors delivers on average a reduction of 89% in the total number of MC simulations, when compared to the exhaustive MC analysis over the full population. In addition to describing a newly developed yield estimation technique, the authors also provide detailed background on automatic analog IC sizing and optimization.

This book presents a systematic study of an emerging field in the development of multi-agent systems. In a wide spectrum of applications, it is now common to see that multiple agents work cooperatively to accomplish a complex task. The book assists the implementation of such applications by promoting the ability of multi-agent systems to track - using local communication only - the mean value of signals of interest, even when these change rapidly with time and when no individual agent has direct access to the average signal across the whole team; for example, when a better estimation/control performance of multi-robot systems has to be guaranteed, it is desirable for each robot to compute or track the averaged changing measurements of all the robots at any time by communicating with only local neighboring robots. The book covers three factors in successful distributed average tracking: algorithm design via nonsmooth and extended PI control; distributed average tracking for double-integrator, general-linear, Euler-Lagrange, and input-saturated dynamics; and applications in dynamic region-following formation control and distributed convex optimization. The book presents both the theory and applications in a general but self-contained manner, making it easy to follow for newcomers to the topic. The content presented fosters research advances in distributed average tracking and inspires future research directions in the field in academia and industry.

Nonlinear Stochastic Processes addresses the frequently-encountered problem of incomplete information. The causes of this problem considered here include: missing measurements; sensor delays and saturation; quantization effects; and signal sampling. Divided into three parts, the text begins with a focus on H filtering and control problems associated with general classes of nonlinear stochastic discrete-time systems. Filtering problems are considered in the second part, and in the third the theory and techniques previously developed are applied to the solution of issues arising in complex networks with the design of sampled-data-based controllers and filters. Among its highlights, the text provides: * a unified framework for filtering and control problems in complex communication networks with limited bandwidth; * new concepts such as random sensor and signal saturations for more realistic modeling; and * demonstration of the use of techniques such as the Hamilton-Jacobi-Isaacs, difference linear matrix, and parameter-dependent matrix inequalities and sums of squares to handle the computational challenges inherent in these systems. The collection of recent research results presented in Nonlinear Stochastic Processes will be of interest to academic researchers in control and signal processing. Graduate students working with communication networks with lossy information and control of stochastic systems will also benefit from reading the book.

This book addresses a key technology for digital information processing: Kalman filtering, which is generally considered to be one of the greatest discoveries of the 20th century. It introduces readers to issues concerning various uncertainties in a single plant, and to corresponding solutions based on adaptive estimation. Further, it discusses in detail the issues that arise when Kalman filtering technology is applied in multi-sensor systems and/or multi-agent systems, especially when various sensors are used in systems like intelligent robots, autonomous cars, smart homes, smart buildings, etc., requiring multi-sensor information fusion techniques. Furthermore, when multiple agents (subsystems) interact with one another, it produces coupling uncertainties, a challenging issue that is addressed here with the aid of novel decentralized adaptive filtering techniques.Overall, the book's goal is to provide readers with a comprehensive investigation into the challenging problem of making Kalman filtering work well in the presence of various uncertainties and/or for multiple sensors/components. State-of-art techniques are introduced, together with a wealth of novel findings. As such, it can be a good reference book for researchers whose work involves filtering and applications; yet it can also serve as a postgraduate textbook for students in mathematics, engineering, automation, and related fields.To read this book, only a basic grasp of linear algebra and probability theory is needed, though experience with least squares, navigation, robotics, etc. would definitely be a plus.

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.

This book represents the best of the first three years of the Society for Chaos Theory in Psychology conferences. While chaos theory has been a topic of considerable interest in the physical and biological sciences, its applications in psychology and related fields have been obscured until recently by its complexity. Nevertheless, a small but rapidly growing community of psychologists, neurobiologists, sociologists, mathematicians, and philosophers have been coming together to discuss its implications and explore its research possibilities.
Chaos theory has been termed the first authentic paradigm shift since the advent of quantum physics. Whether this is true or not, it unquestionably bears profound implications for many fields of thought. These include the cognitive analysis of the mind, the nature of personality, the dynamics of psychotherapy and counseling, understanding brain events and behavioral records, the dynamics of social organization, and the psychology of prediction. To each of these topics, chaos theory brings the perspective of dynamic self-organizing processes of exquisite complexity. Behavior, the nervous system, and social processes exhibit many of the classical characteristics of chaotic systems -- they are deterministic and globally predictable and yet do not submit to precise predictability.
This volume is the first to explore ideas from chaos theory in a broad, psychological perspective. Its introduction, by the prominent neuroscientist Walter Freeman, sets the tone for diverse discussions of the role of chaos theory in behavioral research, the study of personality, psychotherapy and counseling, mathematical cognitive psychology, social organization, systems philosophy, and the understanding of the brain.

This book bridges the gap between advances in the communities of computer science and physics--namely machine learning and statistical physics. It contains diverse but relevant topics in statistical physics, complex systems, network theory, and machine learning. Examples of such topics are: predicting missing links, higher-order generative modeling of networks, inferring network structure by tracking the evolution and dynamics of digital traces, recommender systems, and diffusion processes. The book contains extended versions of high-quality submissions received at the workshop, Dynamics On and Of Complex Networks (doocn.org), together with new invited contributions. The chapters will benefit a diverse community of researchers. The book is suitable for graduate students, postdoctoral researchers and professors of various disciplines including sociology, physics, mathematics, and computer science.

With the advent of the National Curriculum, computer based modelling (CBM) is now a compulsory part of the school curriculum. Teachers are increasingly being encouraged to seek out opportunities for CBM in their own subject and across the curriculum. The new demands on the curriculum have left eachers and teacher trainers concerned as to their lack of experience in the area. This book sets out to provide a comprehensive guide to the area through an examination of a number of funded projects on CBM and their application to the school curriculum, setting them in the context of wider theoretical and practical concerns. It is acknowledged that computers bring about change in the classroom, both in teachers' professional development and innovative practices in teaching and learning. In highlighting how CBM can aid in the effective delivery of the curriculum, this book should be essential reading for teachers and researchers in the field.

Neural Network Modeling offers a cohesive approach to the statistical mechanics and principles of cybernetics as a basis for neural network modeling. It brings together neurobiologists and the engineers who design intelligent automata to understand the physics of collective behavior pertinent to neural elements and the self-control aspects of neurocybernetics. The theoretical perspectives and explanatory projections portray the most current information in the field, some of which counters certain conventional concepts in the visualization of neuronal interactions.

This book gives an in-depth introduction to the areas of modeling, identification, simulation, and optimization. These scientific topics play an increasingly dominant part in many engineering areas such as electrotechnology, mechanical engineering, aerospace, and physics. This book represents a unique and concise treatment of the mutual interactions among these topics.
Techniques for solving general nonlinear optimization problems as they arise in identification and many synthesis and design methods are detailed. The main points in deriving mathematical models via prior knowledge concerning the physics describing a system are emphasized. Several chapters discuss the identification of black-box models. Simulation is introduced as a numerical tool for calculating time responses of almost any mathematical model. The last chapter covers optimization, a generally applicable tool for formulating and solving many engineering problems.

This book presents a new approach for the analysis of chaotic behavior in non-linear dynamical systems, in which output can be represented in quaternion parametrization. It offers a new family of methods for the analysis of chaos in the quaternion domain along with extensive numerical experiments performed on human motion data and artificial data. All methods and algorithms are designed to allow detection of deterministic chaos behavior in quaternion data representing the rotation of a body in 3D space. This book is an excellent reference for engineers, researchers, and postgraduate students conducting research on human gait analysis, healthcare informatics, dynamical systems with deterministic chaos or time series analysis.

Written in the 1980s by one of the fathers of chaos theory, Otto E. Roessler, the manuscript presented in this volume eventually never got published. Almost 40 years later, it remains astonishingly at the forefront of knowledge about chaos theory and many of the examples discussed have never been published elsewhere. The manuscript has now been edited by Christophe Letellier - involved in chaos theory for almost three decades himself, as well as being active in the history of sciences - with a minimum of changes to the original text. Finally released for the benefit of specialists and non-specialists alike, this book is equally interesting from the historical and the scientific points of view: an unconventionally modern approach to chaos theory, it can be read as a classic introduction and short monograph as well as a collection of original insights into advanced topics from this field.

This monograph introduces methods for handling filtering and control problems in nonlinear stochastic systems arising from network-induced phenomena consequent on limited communication capacity. Such phenomena include communication delay, packet dropout, signal quantization or saturation, randomly occurring nonlinearities and randomly occurring uncertainties. The text is self-contained, beginning with an introduction to nonlinear stochastic systems, network-induced phenomena and filtering and control, moving through a collection of the latest research results which focuses on the three aspects of: * the state-of-the-art of nonlinear filtering and control; * recent advances in recursive filtering and sliding mode control; and * their potential for application in networked control systems, and concluding with some ideas for future research work. New concepts such as the randomly occurring uncertainty and the probability-constrained performance index are proposed to make the network models as realistic as possible. The power of combinations of such recent tools as the completing-the-square and sums-of-squares techniques, Hamilton-Jacobi-Isaacs matrix inequalities, difference linear matrix inequalities and parameter-dependent matrix inequalities is exploited in treating the mathematical and computational challenges arising from nonlinearity and stochasticity. Nonlinear Stochastic Systems with Network-Induced Phenomena establishes a unified framework of control and filtering which will be of value to academic researchers in bringing structure to problems associated with an important class of networked system and offering new means of solving them. The significance of the new concepts, models and methods presented for practical control engineering and signal processing will also make it a valuable reference for engineers dealing with nonlinear control and filtering problems.

This book covers the two broad areas of the electronics and electrical aspects of control applications, highlighting the many different types of control systems of relevance to real-life control system design. The control techniques presented are state-of-the-art. In the electronics section, readers will find essential information on microprocessor, microcontroller, mechatronics and electronics control. The low-level assembly programming language performs basic input/output control techniques as well as controlling the stepper motor and PWM dc motor. In the electrical section, the book addresses the complete elevator PLC system design, neural network plant control, load flow analysis, and process control, as well as machine vision topics. Illustrative diagrams, circuits and programming examples and algorithms help to explain the details of the system function design. Readers will find a wealth of computer control and industrial automation practices and applications for modern industries, as well as the educational sector.

This book presents new systems and circuits for implantable biomedical applications, using a non-conventional way to transmit energy and data via ultrasound. The authors discuses the main constrains (e.g. implant size, battery recharge time, data rate, accuracy of the acoustic models) from the definition of the ultrasound system specification to the in-vitro validation.The system described meets the safety requirements for ultrasound exposure limits in diagnostic ultrasound applications, according to FDA regulations. Readers will see how the novel design of power management architecture will meet the constraints set by FDA regulations for maximum energy exposure in the human body. Coverage also includes the choice of the acoustic transducer, driven by optimum positioning and size of the implanted medical device. Throughout the book, links between physics, electronics and medical aspects are covered to give a complete view of the ultrasound system described. Provides a complete, system-level perspective on the use of ultrasound as energy source for medical implants; Discusses system design concerns regarding wireless power transmission and wireless data communication, particularly for a system in which both are performed on the same channel/frequency; Describes an experimental study on implantable battery powered biomedical systems; Presents a fully-integrated, implantable system and hermetically sealed packaging.

The essence of this book can be found in a line written by the ancient Roman Stoic Philosopher Lucius Annaeus Seneca: "Fortune is of sluggish growth, but ruin is rapid". This sentence summarizes the features of the phenomenon that we call "collapse," which is typically sudden and often unexpected, like the proverbial "house of cards." But why are such collapses so common, and what generates them? Several books have been published on the subject, including the well known "Collapse" by Jared Diamond (2005), "The collapse of complex societies" by Joseph Tainter (1998) and "The Tipping Point," by Malcom Gladwell (2000). Why The Seneca Effect? This book is an ambitious attempt to pull these various strands together by describing collapse from a multi-disciplinary viewpoint. The reader will discover how collapse is a collective phenomenon that occurs in what we call today "complex systems," with a special emphasis on system dynamics and the concept of "feedback." From this foundation, Bardi applies the theory to real-world systems, from the mechanics of fracture and the collapse of large structures to financial collapses, famines and population collapses, the fall of entire civilzations, and the most dreadful collapse we can imagine: that of the planetary ecosystem generated by overexploitation and climate change. The final objective of the book is to describe a conclusion that the ancient stoic philosophers had already discovered long ago, but that modern system science has rediscovered today. If you want to avoid collapse you need to embrace change, not fight it. Neither a book about doom and gloom nor a cornucopianist's dream, The Seneca Effect goes to the heart of the challenges that we are facing today, helping us to manage our future rather than be managed by it.

This monograph introduces a newly developed robust-control design technique for a wide class of continuous-time dynamical systems called the "attractive ellipsoid method." Along with a coherent introduction to the proposed control design and related topics, the monograph studies nonlinear affine control systems in the presence of uncertainty and presents a constructive and easily implementable control strategy that guarantees certain stability properties. The authors discuss linear-style feedback control synthesis in the context of the above-mentioned systems. The development and physical implementation of high-performance robust-feedback controllers that work in the absence of complete information is addressed, with numerous examples to illustrate how to apply the attractive ellipsoid method to mechanical and electromechanical systems. While theorems are proved systematically, the emphasis is on understanding and applying the theory to real-world situations. Attractive Ellipsoids in Robust Control will appeal to undergraduate and graduate students with a background in modern systems theory as well as researchers in the fields of control engineering and applied mathematics.

This monograph details basic concepts and tools fundamental for the analysis and synthesis of linear systems subject to actuator saturation and developments in recent research. The authors use a state-space approach and focus on stability analysis and the synthesis of stabilizing control laws in both local and global contexts. Different methods of modeling the saturation and behavior of the nonlinear closed-loop system are given special attention. Various kinds of Lyapunov functions are considered to present different stability conditions. Results arising from uncertain systems and treating performance in the presence of saturation are given. The text proposes methods and algorithms, based on the use of linear programming and linear matrix inequalities, for computing estimates of the basin of attraction and for designing control systems accounting for the control bounds and the possibility of saturation. They can be easily implemented with mathematical software packages.

This book introduces an interesting and alternative way to design absorbing boundary conditions (ABCs) for quantum wave equations, basically the nonlinear Schroedinger equation. The focus of this book is the application of the phase space filter approach to derive accurate radiation conditions for Schroedinger equations. Researchers who are interested in partial differential equations and mathematical physics might find this book appealing.

This book deals with the simulation of the mechanical behavior of engineering structures, mechanisms and components. It presents a set of strategies and tools for formulating the mathematical equations and the methods of solving them using MATLAB. For the same mechanical systems, it also shows how to obtain solutions using a different approaches. It then compares the results obtained with the two methods. By combining fundamentals of kinematics and dynamics of mechanisms with applications and different solutions in MATLAB of problems related to gears, cams, and multilink mechanisms, and by presenting the concepts in an accessible manner, this book is intended to assist advanced undergraduate and mechanical engineering graduate students in solving various kinds of dynamical problems by using methods in MATLAB. It also offers a comprehensive, practice-oriented guide to mechanical engineers dealing with kinematics and dynamics of several mechanical systems.

This volume contains the extended version of selected talks given at the international research workshop "Coping with Complexity: Model Reduction and Data Analysis," Ambleside, UK, August 31 - September 4, 2009. The book is deliberately broad in scope and aims at promoting new ideas and methodological perspectives. The topics of the chapters range from theoretical analysis of complex and multiscale mathematical models to applications in e.g., fluid dynamics and chemical kinetics.

The purpose of this book is to acquaint the reader with the developments in bilinear systems theory and its applications. Bilinear systems can be used to represent a wide range of physical, chemical, biological, and social systems, as well as manufacturing processes, which cannot be effectively modeled under the assumption of linearity. This book provides a unified approach for the identification and control of nonlinear complex objects that can be transformed into bilinear systems, with a focus on the control of open physical processes functioning in a non-equilibrium mode. The material is intended for graduate students, researchers, and specialists engaged in the fields of quantum and molecular computing, control of physical processes, biophysics, superconducting magnetism, physical information science, mathematics, and engineering.