Introduction to Nonlinear Network Theory: Dynamic nonlinear networks
Author | : Leon O. Chua |
Publisher | : |
Total Pages | : 448 |
Release | : 1978 |
Genre | : Electric networks, Nonlinear |
ISBN | : |
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Author | : Leon O. Chua |
Publisher | : |
Total Pages | : 448 |
Release | : 1978 |
Genre | : Electric networks, Nonlinear |
ISBN | : |
Author | : Martin Feinberg |
Publisher | : Springer |
Total Pages | : 475 |
Release | : 2019-01-31 |
Genre | : Mathematics |
ISBN | : 3030038580 |
This book provides an authoritative introduction to the rapidly growing field of chemical reaction network theory. In particular, the book presents deep and surprising theorems that relate the graphical and algebraic structure of a reaction network to qualitative properties of the intricate system of nonlinear differential equations that the network induces. Over the course of three main parts, Feinberg provides a gradual transition from a tutorial on the basics of reaction network theory, to a survey of some of its principal theorems, and, finally, to a discussion of the theory’s more technical aspects. Written with great clarity, this book will be of value to mathematicians and to mathematically-inclined biologists, chemists, physicists, and engineers who want to contribute to chemical reaction network theory or make use of its powerful results.
Author | : Bharathwaj Muthuswamy |
Publisher | : Springer |
Total Pages | : 373 |
Release | : 2018-10-26 |
Genre | : Technology & Engineering |
ISBN | : 3319673254 |
This course-based text revisits classic concepts in nonlinear circuit theory from a very much introductory point of view: the presentation is completely self-contained and does not assume any prior knowledge of circuit theory. It is simply assumed that readers have taken a first-year undergraduate course in differential and integral calculus, along with an elementary physics course in classical mechanics and electrodynamics. Further, it discusses topics not typically found in standard textbooks, such as nonlinear operational amplifier circuits, nonlinear chaotic circuits and memristor networks. Each chapter includes a set of illustrative and worked examples, along with end-of-chapter exercises and lab exercises using the QUCS open-source circuit simulator. Solutions and other material are provided on the YouTube channel created for this book by the authors.
Author | : Andrew Adamatzky |
Publisher | : Springer Science & Business Media |
Total Pages | : 716 |
Release | : 2013-12-18 |
Genre | : Computers |
ISBN | : 3319026305 |
Using memristors one can achieve circuit functionalities that are not possible to establish with resistors, capacitors and inductors, therefore the memristor is of great pragmatic usefulness. Potential unique applications of memristors are in spintronic devices, ultra-dense information storage, neuromorphic circuits and programmable electronics. Memristor Networks focuses on the design, fabrication, modelling of and implementation of computation in spatially extended discrete media with many memristors. Top experts in computer science, mathematics, electronics, physics and computer engineering present foundations of the memristor theory and applications, demonstrate how to design neuromorphic network architectures based on memristor assembles, analyse varieties of the dynamic behaviour of memristive networks and show how to realise computing devices from memristors. All aspects of memristor networks are presented in detail, in a fully accessible style. An indispensable source of information and an inspiring reference text, Memristor Networks is an invaluable resource for future generations of computer scientists, mathematicians, physicists and engineers.
Author | : Janis?aw M. Skowro?ski |
Publisher | : World Scientific |
Total Pages | : 618 |
Release | : 1990 |
Genre | : Science |
ISBN | : 9789810201920 |
The book gives the Liapunov background for the analysis and synthesis (design) of dynamic behaviour of general networks which represent a large class of nonlinear systems, predominantly physical, and in particular mechanical. It is meant to be a self-learning and thought provoking reference text. The introductory chapter refers to the basic concepts of static characteristics and dynamic processes. Subsequent chapters describe various formats of dynamic models and give a reference frame for their behaviour, introduce basic energy relations, fundamental to the dynamic use of the Liapunov method, and describe the method with implementations. The final chapter gives the methods of Liapunov design (synthesis) and control.
Author | : Leon Chua |
Publisher | : Springer Nature |
Total Pages | : 1357 |
Release | : 2019-11-12 |
Genre | : Computers |
ISBN | : 331976375X |
This Handbook presents all aspects of memristor networks in an easy to read and tutorial style. Including many colour illustrations, it covers the foundations of memristor theory and applications, the technology of memristive devices, revised models of the Hodgkin-Huxley Equations and ion channels, neuromorphic architectures, and analyses of the dynamic behaviour of memristive networks. It also shows how to realise computing devices, non-von Neumann architectures and provides future building blocks for deep learning hardware. With contributions from leaders in computer science, mathematics, electronics, physics, material science and engineering, the book offers an indispensable source of information and an inspiring reference text for future generations of computer scientists, mathematicians, physicists, material scientists and engineers working in this dynamic field.
Author | : Leon O. Chua |
Publisher | : World Scientific |
Total Pages | : 362 |
Release | : 1998 |
Genre | : Computers |
ISBN | : 9789810234836 |
Revolutionary and original, this treatise presents a new paradigm of Emergence and Complexity, with applications drawn from numerous disciplines, including artificial life, biology, chemistry, computation, physics, image processing, information science, etc. CNN is an acronym for Cellular Neural Networks when used in the context of brain science, or Cellular Nonlinear Networks, when used in the context of emergence and complexity. A CNN is modeled by cells and interactions: cells are defined as dynamical systems and interactions are defined via coupling laws. The CNN paradigm is a universal Turing machine and includes cellular automata and lattice dynamical systems as special cases. While the CNN paradigm is an example of Reductionism par excellence, the true origin of emergence and complexity is traced to a much deeper new concept called local activity. The numerous complex phenomena unified under this mathematically precise principle include self organization, dissipative structures, synergetics, order from disorder, far-from-thermodynamic equilibrium, collective behaviors, edge of chaos, etc. Written with a high level of exposition, this completely self-contained monograph is profusely illustrated with over 200 stunning color illustrations of emergent phenomena.
Author | : Fernando Corinto |
Publisher | : Springer Nature |
Total Pages | : 438 |
Release | : 2020-10-31 |
Genre | : Technology & Engineering |
ISBN | : 3030556514 |
This book presents a new approach to the study of physical nonlinear circuits and advanced computing architectures with memristor devices. Such a unified approach to memristor theory has never been systematically presented in book form. After giving an introduction on memristor-based nonlinear dynamical circuits (e.g., periodic/chaotic oscillators) and their use as basic computing analogue elements, the authors delve into the nonlinear dynamical properties of circuits and systems with memristors and present the flux-charge analysis, a novel method for analyzing the nonlinear dynamics starting from writing Kirchhoff laws and constitutive relations of memristor circuit elements in the flux-charge domain. This analysis method reveals new peculiar and intriguing nonlinear phenomena in memristor circuits, such as the coexistence of different nonlinear dynamical behaviors, extreme multistability and bifurcations without parameters. The book also describes how arrays of memristor-based nonlinear oscillators and locally-coupled neural networks can be applied in the field of analog computing architectures, for example for pattern recognition. The book will be of interest to scientists and engineers involved in the conceptual design of physical memristor devices and systems, mathematical and circuit models of physical processes, circuits and networks design, system engineering, or data processing and system analysis.