Kronecker Modeling and Analysis of Multidimensional Markovian Systems, Tu?rul Dayar
Автор: Nishisato, Shizuhiko Название: Multidimensional Nonlinear Descriptive Analysis ISBN: 0367390647 ISBN-13(EAN): 9780367390648 Издательство: Taylor&Francis Рейтинг: Цена: 9798.00 р. Наличие на складе: Поставка под заказ.
Описание:
Quantification of categorical, or non-numerical, data is a problem that scientists face across a wide range of disciplines. Exploring data analysis in various areas of research, such as the social sciences and biology, Multidimensional Nonlinear Descriptive Analysis presents methods for analyzing categorical data that are not necessarily sampled randomly from a normal population and often involve nonlinear relations.
This reference not only provides an overview of multidimensional nonlinear descriptive analysis (MUNDA) of discrete data, it also offers new results in a variety of fields. The first part of the book covers conceptual and technical preliminaries needed to understand the data analysis in subsequent chapters. The next two parts contain applications of MUNDA to diverse data types, with each chapter devoted to one type of categorical data, a brief historical comment, and basic skills peculiar to the data types. The final part examines several problems and then concludes with suggestions for future progress.
Covering both the early and later years of MUNDA research in the social sciences, psychology, ecology, biology, and statistics, this book provides a framework for potential developments in even more areas of study.
Описание: This work considers Kronecker-based models with finite as well as countably infinite state spaces for multidimensional Markovian systems by paying particular attention to those whose reachable state spaces are smaller than their product state spaces. Numerical methods for steady-state and transient analysis of Kronecker-based multidimensional Markovian models are discussed in detail together with implementation issues. Case studies are provided to explain concepts and motivate use of methods. Having grown out of research from the past twenty years, this book expands upon the author’s previously published book Analyzing Markov Chains using Kronecker Products (Springer, 2012). The subject matter is interdisciplinary and at the intersection of applied mathematics and computer science. The book will be of use to researchers and graduate students with an understanding of basic linear algebra, probability, and discrete mathematics.
Описание: Suitable for physicists, applied mathematicians, computer scientists, and engineers, this book presents introductory materials, similar to that on the PAMIR website. It also offers details on the use of the programs than is on the website.
Автор: Weirich Paul Название: Decision Space: Multidimensional Utility Analysis ISBN: 0521800099 ISBN-13(EAN): 9780521800099 Издательство: Cambridge Academ Цена: 15206.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: In Decision Space: Multidimensional Utility Analysis, first published in 2001, Paul Weirich increases the power and versatility of utility analysis and in the process advances decision theory. Combining traditional and novel methods of option evaluation into one systematic method, multidimensional utility analysis is a valuable tool in decision theory.
Автор: Paul Fieguth Название: Statistical Image Processing and Multidimensional Modeling ISBN: 1461427053 ISBN-13(EAN): 9781461427056 Издательство: Springer Рейтинг: Цена: 18167.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Examples abound throughout remote sensing (satellite data mapping, data assimilation, climate-change studies, land use), medical imaging (organ segmentation, anomaly detection), computer vision (image classification, segmentation), and other 2D/3D problems (biological imaging, porous media).
Автор: I. Borg; J. Lingoes Название: Multidimensional Similarity Structure Analysis ISBN: 146129147X ISBN-13(EAN): 9781461291473 Издательство: Springer Рейтинг: Цена: 14673.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Multidimensional Similarity Structure Analysis comprises a class of models that represent similarity among entities (for example, variables, items, objects, persons, etc.) in multidimensional space to permit one to grasp more easily the interrelations and patterns present in the data.
Автор: Yu Kang; Yun-Bo Zhao; Ping Zhao Название: Stability Analysis of Markovian Jump Systems ISBN: 9811099863 ISBN-13(EAN): 9789811099861 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Поставка под заказ.
Описание: This book focuses on the stability analysis of Markovian jump systems (MJSs) with various settings and discusses its applications in several different areas. It also presents general definitions of the necessary concepts and an overview of the recent developments in MJSs. Further, it addresses the general robust problem of Markovian jump linear systems (MJLSs), the asynchronous stability of a class of nonlinear systems, the robust adaptive control scheme for a class of nonlinear uncertain MJSs, the practical stability of MJSs and its applications as a modelling tool for networked control systems, Markovian-based control for wheeled mobile manipulators and the jump-linear-quadratic (JLQ) problem of a class of continuous-time MJLSs. It is a valuable resource for researchers and graduate students in the field of control theory and engineering.
Автор: Guoliang Wang; Qingling Zhang; Xinggang Yan Название: Analysis and Design of Singular Markovian Jump Systems ISBN: 3319087223 ISBN-13(EAN): 9783319087221 Издательство: Springer Рейтинг: Цена: 18284.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
This monograph is an up-to-date presentation of the analysis and design of singular Markovian jump systems (SMJSs) in which the transition rate matrix of the underlying systems is generally uncertain, partially unknown and designed. The problems addressed include stability, stabilization, H∞ control and filtering, observer design, and adaptive control. applications of Markov process are investigated by using Lyapunov theory, linear matrix inequalities (LMIs), S-procedure and the stochastic Barbalat's Lemma, among other techniques.
Features of the book include:
- study of the stability problem for SMJSs with general transition rate matrices (TRMs);
- stabilization for SMJSs by TRM design, noise control, proportional-derivative and partially mode-dependent control, in terms of LMIs with and without equation constraints;
- mode-dependent and mode-independent H∞ control solutions with development of a type of disordered controller;
- observer-based controllers of SMJSs in which both the designed observer and controller are either mode-dependent or mode-independent;
- consideration of robust H∞ filtering in terms of uncertain TRM or filter parameters leading to a method for totally mode-independent filtering
- development of LMI-based conditions for a class of adaptive state feedback controllers with almost-certainly-bounded estimated error and almost-certainly-asymptotically-stable corres
ponding closed-loop system states
- applications of Markov process on singular systems with norm bounded uncertainties and time-varying delays
Analysis and Design of Singular Markovian Jump Systems contains valuable reference material for academic researchers wishing to explore the area. The contents are also suitable for a one-semester graduate course.
Описание: Suitable for physicists, applied mathematicians, computer scientists, and engineers, this book presents introductory materials, similar to that on the PAMIR website. It also offers details on the use of the programs than is on the website.
Автор: Jacob Kogan; Charles Nicholas; Marc Teboulle Название: Grouping Multidimensional Data ISBN: 3642066542 ISBN-13(EAN): 9783642066542 Издательство: Springer Рейтинг: Цена: 18167.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Clustering can be used as an independent data mining task to discern intrinsic characteristics of data, or as a preprocessing step with the clustering results then used for classification, correlation analysis, or anomaly detection.Kogan and his co-editors have put together recent advances in clustering large and high-dimension data.
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