A Theory of Learning and Generalization: With Applications to Neural Networks and Control Systems,
Автор: Weber Thomas A. Название: Optimal Control Theory with Applications in Economics ISBN: 0262015730 ISBN-13(EAN): 9780262015738 Издательство: MIT Press Рейтинг: Цена: 10157.00 р. Наличие на складе: Нет в наличии.
Описание:
A rigorous introduction to optimal control theory, with an emphasis on applications in economics.
This book bridges optimal control theory and economics, discussing ordinary differential equations, optimal control, game theory, and mechanism design in one volume. Technically rigorous and largely self-contained, it provides an introduction to the use of optimal control theory for deterministic continuous-time systems in economics. The theory of ordinary differential equations (ODEs) is the backbone of the theory developed in the book, and chapter 2 offers a detailed review of basic concepts in the theory of ODEs, including the solution of systems of linear ODEs, state-space analysis, potential functions, and stability analysis. Following this, the book covers the main results of optimal control theory, in particular necessary and sufficient optimality conditions; game theory, with an emphasis on differential games; and the application of control-theoretic concepts to the design of economic mechanisms. Appendixes provide a mathematical review and full solutions to all end-of-chapter problems.
The material is presented at three levels: single-person decision making; games, in which a group of decision makers interact strategically; and mechanism design, which is concerned with a designer's creation of an environment in which players interact to maximize the designer's objective. The book focuses on applications; the problems are an integral part of the text. It is intended for use as a textbook or reference for graduate students, teachers, and researchers interested in applications of control theory beyond its classical use in economic growth. The book will also appeal to readers interested in a modeling approach to certain practical problems involving dynamic continuous-time models.
Автор: Johann M.Ph. Schumann; Yan Liu Название: Applications of Neural Networks in High Assurance Systems ISBN: 3642262694 ISBN-13(EAN): 9783642262692 Издательство: Springer Цена: 19589.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Gert Schubring Название: Conflicts Between Generalization, Rigor, and Intuition ISBN: 1441919872 ISBN-13(EAN): 9781441919878 Издательство: Springer Рейтинг: Цена: 27251.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This volume is, as may be readily apparent, the fruit of many years' labor in archives and libraries, unearthing rare books, researching Nachlasse, and above all, systematic comparative analysis of fecund sources. The work not only demanded much time in preparation, but was also interrupted by other duties, such as time spent as a guest professor at universities abroad, which of course provided welcome opportunities to present and discuss the work, and in particular, the organizing of the 1994 International Gramann Conference and the subsequent editing of its proceedings. If it is not possible to be precise about the amount of time spent on this work, it is possible to be precise about the date of its inception. In 1984, during research in the archive of the Ecole polytechnique, my attention was drawn to the way in which the massive rupture that took place in 1811--precipitating the change back to the synthetic method and replacing the limit method by the method of the quantites infiniment petites--significantly altered the teaching of analysis at this first modern institution of higher education, an institution originally founded as a citadel of the analytic method.
Автор: Tercan Название: Module Theory, Extending Modules and Generalizations ISBN: 3034809506 ISBN-13(EAN): 9783034809504 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The main focus of this monograph is to offer a comprehensive presentation of known and new results on various generalizations of CS-modules and CS-rings. Extending (or CS) modules are generalizations of injective (and also semisimple or uniform) modules. While the theory of CS-modules is well documented in monographs and textbooks, results on generalized forms of the CS property as well as dual notions are far less present in the literature.With their work the authors provide a solid background to module theory, accessible to anyone familiar with basic abstract algebra. The focus of the book is on direct sums of CS-modules and classes of modules related to CS-modules, such as relative (injective) ejective modules, (quasi) continuous modules, and lifting modules. In particular, matrix CS-rings are studied and clear proofs of fundamental decomposition results on CS-modules over commutative domains are given, thus complementing existing monographs in this area.Open problems round out the work and establish the basis for further developments in the field. The main text is complemented by a wealth of examples and exercises.
Автор: Erdal Kayacan Название: Fuzzy Neural Networks for Real Time Control Applications ISBN: 0128026871 ISBN-13(EAN): 9780128026878 Издательство: Elsevier Science Рейтинг: Цена: 12294.00 р. Наличие на складе: Поставка под заказ.
Описание:
AN INDISPENSABLE RESOURCE FOR ALL THOSE WHO DESIGN AND IMPLEMENT TYPE-1 AND TYPE-2 FUZZY NEURAL NETWORKS IN REAL TIME SYSTEMS
Delve into the type-2 fuzzy logic systems and become engrossed in the parameter update algorithms for type-1 and type-2 fuzzy neural networks and their stability analysis with this book
Not only does this book stand apart from others in its focus but also in its application-based presentation style. Prepared in a way that can be easily understood by those who are experienced and inexperienced in this field. Readers can benefit from the computer source codes for both identification and control purposes which are given at the end of the book.
A clear and an in-depth examination has been made of all the necessary mathematical foundations, type-1 and type-2 fuzzy neural network structures and their learning algorithms as well as their stability analysis.
You will find that each chapter is devoted to a different learning algorithm for the tuning of type-1 and type-2 fuzzy neural networks; some of which are:
- Gradient descent
- Levenberg-Marquardt
- Extended Kalman filter
In addition to the aforementioned conventional learning methods above, number of novel sliding mode control theory-based learning algorithms, which are simpler and have closed forms, and their stability analysis have been proposed. Furthermore, hybrid methods consisting of particle swarm optimization and sliding mode control theory-based algorithms have also been introduced.
The potential readers of this book are expected to be the undergraduate and graduate students, engineers, mathematicians and computer scientists. Not only can this book be used as a reference source for a scientist who is interested in fuzzy neural networks and their real-time implementations but also as a course book of fuzzy neural networks or artificial intelligence in master or doctorate university studies. We hope that this book will serve its main purpose successfully.
Автор: Johann M.Ph. Schumann; Yan Liu Название: Applications of Neural Networks in High Assurance Systems ISBN: 3642106897 ISBN-13(EAN): 9783642106897 Издательство: Springer Рейтинг: Цена: 23508.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This is the first book to directly address a key part of neural network technology: state-of-the-art methods used to pass the tough verification and validation standards required in many safety-critical applications.
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