Beyond Traditional Probabilistic Data Processing Techniques: Interval, Fuzzy Etc. Methods and Their Applications, Kosheleva Olga, Shary Sergey P., Xiang Gang
Описание: Accordingly, the book offers a valuable guide for researchers and practitioners interested in data processing under uncertainty, and an introduction to the latest trends and techniques in this area, suitable for graduate students.
Описание: On various examples ranging from geosciences to environmental sciences, thisbook explains how to generate an adequate description of uncertainty, how to justifysemiheuristic algorithms for processing uncertainty, and how to make these algorithmsmore computationally efficient.
Описание: Introduction.- Towards a More Adequate Description of Uncertainty.- Towards Justification of Heuristic Techniques for Processing Uncertainty.- Towards More Computationally Efficient Techniques for Processing Uncertainty.- Towards Better Ways of Extracting Information About Uncertainty from Data.
How can we solve engineering problems while taking into account data characterized by different types of measurement and estimation uncertainty: interval, probabilistic, fuzzy, etc.? This book provides a theoretical basis for arriving at such solutions, as well as case studies demonstrating how these theoretical ideas can be translated into practical applications in the geosciences, pavement engineering, etc.
In all these developments, the authors’ objectives were to provide accurate estimates of the resulting uncertainty; to offer solutions that require reasonably short computation times; to offer content that is accessible for engineers; and to be sufficiently general - so that readers can use the book for many different problems. The authors also describe how to make decisions under different types of uncertainty.
The book offers a valuable resource for all practical engineers interested in better ways of gauging uncertainty, for students eager to learn and apply the new techniques, and for researchers interested in processing heterogeneous uncertainty.
Описание: Introduction.- How to Get More Accurate Estimates.- How to Speed Up Computations.- Towards a Better Understandability of Uncertainty-Estimating Algorithms.- How General Can We Go: What Is Computable and What Is Not.- Decision Making Under Uncertainty.- Conclusions.
Автор: Chakraverty, Snehashish Название: Computational Interval Methods For Engineering Applications ISBN: 0128178582 ISBN-13(EAN): 9780128178584 Издательство: Elsevier Science Рейтинг: Цена: 21812.00 р. Наличие на складе: Поставка под заказ.
Описание:
Computational Interval Methods for Engineering Applications explains how to use classical and advanced interval arithmetic to solve differential equations for a wide range of scientific and engineering problems. In mathematical models where there are variables and parameters of uncertain value, interval methods can be used as an efficient tool for handling this uncertainty. In addition, it can produce rigorous enclosures of solutions of practical problems governed by mathematical equations. Other topics discussed in the book include linear differential equations in areas such as robotics, control theory, and structural dynamics, and in nonlinear oscillators, such as Duffing and Van der Pol.
The chaotic behavior of the enclosure of oscillators is also covered, as are static and dynamic analysis of engineering problems using the interval system of linear equations and eigenvalue problems, thus making this a comprehensive resource.
Автор: Olga Kosheleva; Karen Villaverde Название: How Interval and Fuzzy Techniques Can Improve Teaching ISBN: 3662559919 ISBN-13(EAN): 9783662559918 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Introduction: Need for Interval and Fuzzy Techniques in Math and Science Education.-Part I How to Motivate Students.-How to Motivate Students? .-Need to Understand the Presence of Uncertainty: Emphasizing Paradoxes as a (Seemingly Paradoxical) Way to Enhance the Learning of (Strict) Mathematics .-Uncertainty-Related Example Explaining Why Geometry Is Useful: Geometry of a Plane .-Uncertainty-Related Example Explaining Why Calculus Is Useful: Example of the Mean Value Theorem .-How to Enhance Student Motivations by Borrowing from Ancient Tradition: Egyptian Fractions .-How to Enhance Student Motivations by Borrowing from Ancient Tradition: Mayan and Babylonian Arithmetics .-How to Enhance Student Motivations by Borrowing from Ancient Tradition: Babylonian Method of Computing the Square Root .-How to Enhance Student Motivations by Borrowing from Ancient Tradition: Russian Peasant Multiplication Algorithm .-How to Enhance Student Motivations by Borrowing from Modern Practices: Geometric Approach to Error-Less Counting .-How to Enhance Student Motivations by Borrowing from Modern Practices: Can We Learn Algorithms from People Who Compute Fast.-How to Enhance a General Student Motivation to Study: Asymmetric Paternalism .-Financial Motivation: How to Incentivize Students to Graduate Faster.-Part II In What Order to Present the Material.-In What Order to Present the Material.-Spiral Curriculum: Towards Mathematical Foundations.-How Much Time to Allocate to Each Topic?.-What is Wrong with Teaching to the Test: Uncertainty Techniques Help in Understanding the Controversy.-In What Order to Present the Material: Fractal Approach.-How AI-Type Uncertainty Ideas Can Improve Inter-Disciplinary Education and Collaboration: Lessons from a Case Study.-In What Order to Present the Material Within Each Topic: Concrete-First vs. Abstract-First .-Part III How to Select an Appropriate Way of Teaching Each Topic.-How to Select an Appropriate Way of Teaching Each Topic .-What is the Best Way to Distribute the Teacher's Efforts Among Students .-What is the Best Way to Allocate Teacher's Efforts: How Accurately Should We Write on the Board? When Marking Comments on Student Papers? .-How to Divide Students into Groups so as to Optimize Learning .-How to Divide Students into Groups: Importance of Diversity and Need for Intelligent Techniques to Further Enhance the Advantage of Groups with Diversity in Problem Solving .-A Minor but Important Aspect of Teaching Large Classes: When to Let in Late Students? .-Part IV How to Assess Students, Teachers, and Teaching Techniques.-How to Assess Students, Teachers, and Teaching Techniques.-How to Assess Students: Rewarding Results or Rewarding Efforts?.-How to Assess Students: Assess Frequently.-How to Assess Students: Surprise Them.-How to Assess Individual Contributions to a Group Project.-How to Access Students's Readiness for the Next Class.-How to Assess Students: Beyond Weighted Average.-How to Assess a Class .-How to Assess Teachers.-How to Assess Teaching Teachniques.-How to Assess Universities: Defining Average Class Size in a Way Which Is Most Adequate for Teaching Effectiveness.-Conclusions .
Описание: Targeted audience - Specialists in numerical computations, especially in numerical optimiza- tion, who are interested in designing algorithms with automatie result ver- ification, and who would therefore be interested in knowing how general their algorithms caIi in principle be. - Mathematicians and computer scientists who are interested in the theory 0/ computing and computational complexity, especially computational com- plexity of numerical computations. - Students in applied mathematics and computer science who are interested in computational complexity of different numerical methods and in learning general techniques for estimating this computational complexity. The book is written with all explanations and definitions added, so that it can be used as a graduate level textbook. What this book .is about Data processing. In many real-life situations, we are interested in the value of a physical quantity y that is diflicult (or even impossible) to measure directly. For example, it is impossible to directly measure the amount of oil in an oil field or a distance to a star. Since we cannot measure such quantities directly, we measure them indirectly, by measuring some other quantities Xi and using the known relation between y and Xi'S to reconstruct y. The algorithm that transforms the results Xi of measuring Xi into an estimate fj for y is called data processing.
Описание: Describes for what classes of problems interval computations (ie data processing with automatic results verification) are feasible, and when they are intractable.
Описание: This is the first book to provide a comprehensive and systematic introduction to the ranking methods for interval-valued intuitionistic fuzzy sets, multi-criteria decision-making methods with interval-valued intuitionistic fuzzy sets, and group decision-making methods with interval-valued intuitionistic fuzzy preference relations.
Описание: This is the first book to provide a comprehensive and systematic introduction to the ranking methods for interval-valued intuitionistic fuzzy sets, multi-criteria decision-making methods with interval-valued intuitionistic fuzzy sets, and group decision-making methods with interval-valued intuitionistic fuzzy preference relations.
Автор: P?kala Название: Uncertainty Data in Interval-Valued Fuzzy Set Theory ISBN: 3319939092 ISBN-13(EAN): 9783319939094 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book offers an introduction to fuzzy sets theory and their operations, with a special focus on aggregation and negation functions. Particular attention is given to interval-valued fuzzy sets and Atanassov`s intuitionistic fuzzy sets and their use in uncertainty models involving imperfect or unknown information.
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