Algorithms of Estimation for Nonlinear Systems, Rafael Mart?nez-Guerra; Christopher Diego Cruz-Anc
Автор: Sung Название: Algorithms in Bioinformatics ISBN: 1420070339 ISBN-13(EAN): 9781420070330 Издательство: Taylor&Francis Рейтинг: Цена: 13779.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Presents an introduction to the algorithmic techniques applied in bioinformatics. For each topic, this title details the biological motivation, defines the corresponding computational problems, and includes examples to illustrate each algorithm.
Описание: The definition of "photonics" has been broadened in recent years to include nonlinear and quantum optics, today usually based on laser light. This book covers the fundamental properties of photon and light beams, both experimentally and theoretically. It covers the essentials of linear interactions and most of the nonlinear interactions between light and matter in both the transparent and absorbing cases. It also provides a basic knowledge of modern lasers, as well as the principles of nonlinear optical spectroscopy. It is self-consistent and enriched by a large number of calculated illustrations, examples, and descriptive tables. Graduate students in physics and electrical engineering, as well as other sciences, will find this book a thorough introduction to the field, while for lecturers and scientists it offers a rich source of useful information and a ready-to-hand reference. About 4000 references open access to original literature.The second edition is completely revised and enlarged.
A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.
Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context.
After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals.
Описание: 22 papers on control of nonlinear partial differential equations highlight the area from a broad variety of viewpoints. A significant part of the volume is devoted to applications in engineering, continuum mechanics and population biology.
Автор: Mukhopadhyay Sambit, Morris Edward, Arulkumaran Sa Название: Algorithms for Obstetrics and Gynaecology ISBN: 0199651396 ISBN-13(EAN): 9780199651399 Издательство: Oxford Academ Рейтинг: Цена: 8129.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Algorithms in Obstetrics and Gynaecology presents the core knowledge needed to tackle all situations in obstetrics and gynaecology, in a structured fashion. All algorithms are designed to support rapid decision making in the most clinically relevant situations to minimise the risks of a poor outcome.
Описание: From the reviews: "This book is concerned with the application of methods from dynamical systems and bifurcation theories to the study of nonlinear oscillations. Chapter 1 provides a review of basic results in the theory of dynamical systems, covering both ordinary differential equations and discrete mappings. Chapter 2 presents 4 examples from nonlinear oscillations. Chapter 3 contains a discussion of the methods of local bifurcation theory for flows and maps, including center manifolds and normal forms. Chapter 4 develops analytical methods of averaging and perturbation theory. Close analysis of geometrically defined two-dimensional maps with complicated invariant sets is discussed in chapter 5. Chapter 6 covers global homoclinic and heteroclinic bifurcations. The final chapter shows how the global bifurcations reappear in degenerate local bifurcations and ends with several more models of physical problems which display these behaviors." #Book Review - Engineering Societies Library, New York#1 "An attempt to make research tools concerning `strange attractors' developed in the last 20 years available to applied scientists and to make clear to research mathematicians the needs in applied works. Emphasis on geometric and topological solutions of differential equations. Applications mainly drawn from nonlinear oscillations." #American Mathematical Monthly#2
Автор: Sanders J. A., Verhulst F., Murdock J. Название: Averaging Methods in Nonlinear Dynamical Systems ISBN: 0387489169 ISBN-13(EAN): 9780387489162 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Perturbation theory and in particular normal form theory has shown strong growth during the last decades. So it is not surprising that the authors have presented an extensive revision of the first edition of the Averaging Methods in Nonlinear Dynamical Systems book. There are many changes, corrections and updates in chapters on Basic Material and Asymptotics, Averaging, and Attraction. Chapters on Periodic Averaging and Hyperbolicity, Classical (first level) Normal Form Theory, Nilpotent (classical) Normal Form, and Higher Level Normal Form Theory are entirely new and represent new insights in averaging, in particular its relation with dynamical systems and the theory of normal forms. Also new are surveys on invariant manifolds in Appendix C and averaging for PDEs in Appendix E. Since the first edition, the book has expanded in length and the third author, James Murdock has been added.Review of First Edition"One of the most striking features of the book is the nice collection of examples, which range from the very simple to some that are elaborate, realistic, and of considerable practical importance. Most of them are presented in careful detail and are illustrated with profuse, illuminating diagrams." - Mathematical Reviews
Автор: Heidar A. Talebi; Farzaneh Abdollahi; Rajni V. Pat Название: Neural Network-Based State Estimation of Nonlinear Systems ISBN: 1441914374 ISBN-13(EAN): 9781441914378 Издательство: Springer Рейтинг: Цена: 15672.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This text offers neural network schemes for state estimation, system identification and fault detection. It covers mathematical proof of stability, experimental evaluation, and robustness against unmolded dynamics, external disturbances and measurement noises.
Автор: Ivan Nagy; Evgenia Suzdaleva Название: Algorithms and Programs of Dynamic Mixture Estimation ISBN: 3319646702 ISBN-13(EAN): 9783319646701 Издательство: Springer Рейтинг: Цена: 7685.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides a general theoretical background for constructing the recursive Bayesian estimation algorithms for mixture models.
Автор: Pedro Larra?aga; Jos? A. Lozano Название: Estimation of Distribution Algorithms ISBN: 1461356040 ISBN-13(EAN): 9781461356042 Издательство: Springer Рейтинг: Цена: 27950.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Estimation of Distribution Algorithms: A New Tool for EvolutionaryComputation is devoted to a new paradigm for evolutionary computation, named estimation of distribution algorithms (EDAs). This new class of algorithms generalizes genetic algorithms by replacing the crossover and mutation operators with learning and sampling from the probability distribution of the best individuals of the population at each iteration of the algorithm. Working in such a way, the relationships between the variables involved in the problem domain are explicitly and effectively captured and exploited. This text constitutes the first compilation and review of the techniques and applications of this new tool for performing evolutionary computation. Estimation of Distribution Algorithms: A NewTool for Evolutionary Computation is clearly divided into three parts. Part I is dedicated to the foundations of EDAs. In this part, after introducing some probabilistic graphical models - Bayesian and Gaussian networks - a review of existing EDA approaches is presented, as well as some new methods based on more flexible probabilistic graphical models. A mathematical modeling of discrete EDAs is also presented. Part II covers several applications of EDAs in some classical optimization problems: the travelling salesman problem, the job scheduling problem, and the knapsack problem. EDAs are also applied to the optimization of some well-known combinatorial and continuous functions. Part III presents the application of EDAs to solve some problems that arise in the machine learning field: feature subset selection, feature weighting in K-NN classifiers, rule induction, partial abductive inference in Bayesian networks, partitional clustering, and the search for optimal weights in artificial neural networks. Estimation of Distribution Algorithms: A New Tool for EvolutionaryComputation is a useful and interesting tool for researchers working in the field of evolutionary computation and for engineers who face real-world optimization problems. This book may also be used by graduate students and researchers in computer science. ... I urge those who are interested in EDAs to study thiswell-crafted book today.' David E. Goldberg, University of Illinois Champaign-Urbana.
Автор: Joachim Inkmann Название: Conditional Moment Estimation of Nonlinear Equation Systems ISBN: 3540412077 ISBN-13(EAN): 9783540412076 Издательство: Springer Рейтинг: Цена: 11179.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Generalized method of moments (GMM) estimation of nonlinear systems has two important advantages over conventional maximum likelihood (ML) estimation: GMM estimation usually requires less restrictive distributional assumptions and remains computationally attractive when ML estimation becomes burdensome or even impossible.
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