Автор: Tang Ming Название: Parametric Building Design Using Autodesk Maya ISBN: 041564447X ISBN-13(EAN): 9780415644471 Издательство: Taylor&Francis Рейтинг: Цена: 7348.00 р. Наличие на складе: Поставка под заказ.
Описание: Including case studies from Zaha Hadid Architects, Greg Lynn Form, Gage Clemenceau Architects, Tang & Yang Architects, as well as step by step exercises, and demonstration projects; and crucially a fantastic online resource which includes video tutorials, scripts, and Maya source files, this book explores the application of Autodesk Maya in the design process.
Автор: PARAMETRIC; BROSOWSKI; DEUTSCH Название: Parametric Optimization and Approximation ISBN: 3034862555 ISBN-13(EAN): 9783034862554 Издательство: Springer Рейтинг: Цена: 11179.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Pistikopoulos Название: Multi-Parametric Optimization and Control ISBN: 1119265185 ISBN-13(EAN): 9781119265184 Издательство: Wiley Рейтинг: Цена: 17258.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Recent developments in multi-parametric optimization and control
Multi-Parametric Optimization and Control provides comprehensive coverage of recent methodological developments for optimal model-based control through parametric optimization. It also shares real-world research applications to support deeper understanding of the material.
Researchers and practitioners can use the book as reference. It is also suitable as a primary or a supplementary textbook. Each chapter looks at the theories related to a topic along with a relevant case study. Topic complexity increases gradually as readers progress through the chapters. The first part of the book presents an overview of the state-of-the-art multi-parametric optimization theory and algorithms in multi-parametric programming. The second examines the connection between multi-parametric programming and model-predictive control--from the linear quadratic regulator over hybrid systems to periodic systems and robust control.
The third part of the book addresses multi-parametric optimization in process systems engineering. A step-by-step procedure is introduced for embedding the programming within the system engineering, which leads the reader into the topic of the PAROC framework and software platform. PAROC is an integrated framework and platform for the optimization and advanced model-based control of process systems.
Uses case studies to illustrate real-world applications for a better understanding of the concepts presented Covers the fundamentals of optimization and model predictive control Provides information on key topics, such as the basic sensitivity theorem, linear programming, quadratic programming, mixed-integer linear programming, optimal control of continuous systems, and multi-parametric optimal control
An appendix summarizes the history of multi-parametric optimization algorithms. It also covers the use of the parametric optimization toolbox (POP), which is comprehensive software for efficiently solving multi-parametric programming problems.
Описание: The Three Revolutions in Parametric Statistical Inference.- The Three Revolutions in Parametric Statistical Inference.- Binomial Statistical Inference.- James Bernoulli's Law of Large Numbers for the Binomial, 1713, and Its Generalization.- De Moivre's Normal Approximation to the Binomial, 1733, and Its Generalization.- Bayes's Posterior Distribution of the Binomial Parameter and His Rule for Inductive Inference, 1764.- Statistical Inference by Inverse Probability.- Laplace's Theory of Inverse Probability, 1774-1786.- A Nonprobabilistic Interlude: The Fitting of Equations to Data, 1750-1805.- Gauss's Derivation of the Normal Distribution and the Method of Least Squares, 1809.- Credibility and Confidence Intervals by Laplace and Gauss.- The Multivariate Posterior Distribution.- Edgeworth's Genuine Inverse Method and the Equivalence of Inverse and Direct Probability in Large Samples, 1908 and 1909.- Criticisms of Inverse Probability.- The Central Limit Theorem and Linear Minimum Variance Estimation by Laplace and Gauss.- Laplace's Central Limit Theorem and Linear Minimum Variance Estimation.- Gauss's Theory of Linear Minimum Variance Estimation.- Error Theory. Skew Distributions. Correlation. Sampling Distributions.- The Development of a Frequentist Error Theory.- Skew Distributions and the Method of Moments.- Normal Correlation and Regression.- Sampling Distributions Under Normality, 1876-1908.- The Fisherian Revolution, 1912-1935.- Fisher's Early Papers, 1912-1921.- The Revolutionary Paper, 1922.- Studentization, the F Distribution, and the Analysis of Variance, 1922-1925.- The Likelihood Function, Ancillarity, and Conditional Inference.
Автор: M.C. Cartmell Название: Introduction to Linear, Parametric and Non-Linear Vibrations ISBN: 0412307308 ISBN-13(EAN): 9780412307300 Издательство: Springer Рейтинг: Цена: 23508.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: A useful source of practical examples of parametric excitations, treated in a modern fashion. Here, the author presents the usually complicated and difficult subjects of parametric and nonlinear vibrations in a concise, and easy-to-understand manner.
Автор: E. Brodsky; B.S. Darkhovsky Название: Non-Parametric Statistical Diagnosis ISBN: 9048154650 ISBN-13(EAN): 9789048154654 Издательство: Springer Рейтинг: Цена: 34519.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book has a distinct philosophy and it is appropriate to make it explicit at the outset. In our view almost all classic statistical inference is based upon the assumption (explicit or implicit) that there exists a fixed probabilistic mechanism of data generation. Unlike classic statistical inference, this book is devoted to the statistical analysis of data about complex objects with more than one probabilistic mechanism of data generation. We think that the exis- tence of more than one data generation process (DGP) is the most important characteristic of com plex systems. When the hypothesis of statistical homogeneity holds true, Le., there exists only one mechanism of data generation, all statistical inference is based upon the fundamentallaws of large numbers. However, the situation is completely different when the probabilistic law of data generation can change (in time or in the phase space). In this case all data obtained must be 'sorted' in subsamples generated by different probabilistic mechanisms. Only after such classification we can make correct inferences about all DGPs. There exists yet another type of problem for complex systems. Here it is important to detect possible (but unpredictable) changes of DGPs on-line with data collection. Since the complex system can change the probabilistic mechanism of data generation, the correct statistical analysis of such data must begin with decisions about possible changes in DGPs.
Автор: Igor Boiko Название: Non-parametric Tuning of PID Controllers ISBN: 1447160460 ISBN-13(EAN): 9781447160465 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book joins new modifications of classical relay feedback test with application-specific optimal tuning rules to form a non-parametric test-and-tuning method. Includes problems of optimization and identification accompanied by downloadable MATLAB (R) code.
Автор: E. Brodsky; B.S. Darkhovsky Название: Non-Parametric Statistical Diagnosis ISBN: 0792363280 ISBN-13(EAN): 9780792363286 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: A systematic account of various problems of statistical diagnostics - in other words, the detection of changes in probabilistic characteristics of random processes and fields. Methods of solving such problems are proposed, based upon a unified non-parametric approach.
ООО "Логосфера " Тел:+7(495) 980-12-10 www.logobook.ru
