WileyVCH  Mathematics
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Fuzzy Logic Applications in Computer Science and Mathematics
FUZZY LOGIC APPLICATIONS IN COMPUTER SCIENCE AND MATHEMATICSTICS The prime objective of developing this book is to provide meticulous details about the basic and advanced concepts of fuzzy logic and its allaround applications to different fields of mathematics and engineering. The basic steps of fuzzy inference systems starting from the core foundation of the fuzzy concepts are presented in this book. The fuzzy theory is a mathematical concept and, at the same time, it is applied to many versatile engineering fields and research domains related to computer science. The fuzzy system offers some knowledge about uncertainty and is also related to the theory of probability. A fuzzy logicbased model acts as the classifier for many different types of data belonging to several classes. Covered in this book are topics such as the fundamental concepts of mathematics, fuzzy logic concepts, probability and possibility theories, and evolutionary computing to some extent. The combined fields of neural network and fuzzy domain (known as the neurofuzzy system) are explained and elaborated. Each chapter has been produced in a very lucid manner, with grading from simple to complex to accommodate the anticipated different audiences. The applicationoriented approach is the unique feature of this book. Audience This book will be read and used by a broad audience including applied mathematicians, computer scientists, and industry engineers. [304 Pages, Hardcover]
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Grundlagen kontinuierlicher Symmetrien
Das neue Buch von Franck Laloë stellt einen symmetriebasierten Ansatz vor, um die Quantenmechanik auf einer fundamentalen Ebene zu verstehen, und liefert die dazugehörigen Rechentechniken, um fortgeschrittene Kurse über Kernphysik, Quantenoptik und Festkörperphysik zu meistern. [XII, 538 Pages, Hardcover]
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An Introduction to Optimization
An Introduction to Optimization Accessible introductory textbook on optimization theory and methods, with an emphasis on engineering design, featuring MATLAB(r) exercises and worked examples Fully updated to reflect modern developments in the field, the Fifth Edition of An Introduction to Optimization fills the need for an accessible, yet rigorous, introduction to optimization theory and methods, featuring innovative coverage and a straightforward approach. The book begins with a review of basic definitions and notations while also providing the related fundamental background of linear algebra, geometry, and calculus. With this foundation, the authors explore the essential topics of unconstrained optimization problems, linear programming problems, and nonlinear constrained optimization. In addition, the book includes an introduction to artificial neural networks, convex optimization, multiobjective optimization, and applications of optimization in machine learning. Numerous diagrams and figures found throughout the book complement the written presentation of key concepts, and each chapter is followed by MATLAB(r) exercises and practice problems that reinforce the discussed theory and algorithms. The Fifth Edition features a new chapter on Lagrangian (nonlinear) duality, expanded coverage on matrix games, projected gradient algorithms, machine learning, and numerous new exercises at the end of each chapter. An Introduction to Optimization includes information on: * The mathematical definitions, notations, and relations from linear algebra, geometry, and calculus used in optimization * Optimization algorithms, covering onedimensional search, randomized search, and gradient, Newton, conjugate direction, and quasiNewton methods * Linear programming methods, covering the simplex algorithm, interior point methods, and duality * Nonlinear constrained optimization, covering theory and algorithms, convex optimization, and Lagrangian duality * Applications of optimization in machine learning, including neural network training, classification, stochastic gradient descent, linear regression, logistic regression, support vector machines, and clustering. An Introduction to Optimization is an ideal textbook for a one or twosemester senior undergraduate or beginning graduate course in optimization theory and methods. The text is also of value for researchers and professionals in mathematics, operations research, electrical engineering, economics, statistics, and business. [672 Pages, Hardcover]
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Power System Simulation Using SemiAnalytical Methods
Robust coverage of semianalytical and traditional numerical methods for power system simulation In Power System Simulation Using SemiAnalytical Methods, distinguished researcher Dr. Kai Sun delivers a comprehensive treatment of semianalytical simulation and current semianalytical methods for power systems. The book presents semianalytical solutions on power system dynamics via mathematical tools, and covers parallel contingency analysis and simulations. The author offers an overview of power system simulation and contingency analysis supported by data, tables, illustrations, and case studies on realistic power systems and experiments. Readers will find opensource code in MATLAB along with examples for key algorithms introduced in the book. You'll also find: * A thorough background on power system simulation, including models, numerical solution methods, and semianalytical solution methods * Comprehensive explorations of semianalytical power system simulation via a variety of mathematical methods such as the Adomian decomposition, differential transformation, homotopy analysis and holomorphic embedding methods. * Practical discussions of semianalytical simulations for realistic largescale power grids * Fulsome treatments of parallel power system simulation Perfect for power engineers and applied mathematicians with an interest in highperformance simulation of power systems and other largescale network systems, Power System Simulation Using SemiAnalytical Methods will also benefit researchers and postgraduate students studying power system engineering. [368 Pages, Hardcover]
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IGA
Isogeometric analysis (IGA) consists of using the same higherorder and smooth spline functions for the representation of geometry in Computer Aided Design as for the approximation of solution fields in Finite Element Analysis. Now, almost twenty years after its creation, substantial works are being reported in IGA, making it very competitive in scientific computing. This book proposes to use IGA jointly with standard finite element methods (FEM), presenting IGA as a projection of FEM on a more regular reduced basis. By shedding new light on how IGA relates to FEM, we can see how IGA can be implemented on top of an FE code in order to improve the solution of problems that require more regularity. This is illustrated by using IGA with FEM in a noninvasive fashion to perform efficient and robust multiscale global/local simulations in solid mechanics. Furthermore, we show that IGA can regularize the inverse problem of FE digital image correlation in experimental mechanics. [240 Pages, Hardcover]
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