Описание: The use of fuzzy logic has become prominent in a variety of fields and applications. By implementing these logic sets, problems and uncertainties are more effectively resolved.Emerging Research on Applied Fuzzy Sets and Intuitionistic Fuzzy Matrices is a pivotal reference source for the latest scholarly perspectives on the interdisciplinary use of fuzzy logic theory, focusing on the application of sets and matrices. Highlighting theoretical framework and empirical research findings, this book is ideally designed for academics, practitioners, upper-level students, and professionals interested in an innovative overview of fuzzy logic sets and matrices.
Автор: Emmanuel Vincent; Arie Yeredor; Zbyn?k Koldovsk?; Название: Latent Variable Analysis and Signal Separation ISBN: 3319224816 ISBN-13(EAN): 9783319224817 Издательство: Springer Рейтинг: Цена: 8944.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the proceedings of the 12th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICS 2015, held in Liberec, Czech Republic, in August 2015. Five special topics are addressed: tensor-based methods for blind signal separation;
Автор: Huaping Liu; Fuchun Sun Название: Robotic Tactile Perception and Understanding ISBN: 981106170X ISBN-13(EAN): 9789811061707 Издательство: Springer Рейтинг: Цена: 13275.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book introduces the challenges of robotic tactile perception and task understanding, and describes an advanced approach based on machine learning and sparse coding techniques.
Автор: Kazuo Murota Название: Matrices and Matroids for Systems Analysis ISBN: 3642039936 ISBN-13(EAN): 9783642039935 Издательство: Springer Рейтинг: Цена: 23058.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book offers a unique introduction to matroid theory, emphasizing motivations from matrix theory and applications to systems analysis. It serves also as a comprehensive presentation of the theory and application of mixed matrices.
Автор: Frutuoso G. M. Silva; Quoc Trong Nguyen; Ac?cio F. Название: Ultimate Performance Analysis Tool (uPATO) ISBN: 3319997521 ISBN-13(EAN): 9783319997520 Издательство: Springer Рейтинг: Цена: 7685.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book introduces the ultimate performance analysis tool (uPATO) as a new software to compute social network metrics in the scope of team sports analysis. The reader will identify the algorithms to test the general properties of the team, the co-dependencies and the centrality levels of players, i.e. to evaluate the individual, sub-group, and team performance analysis. As uPATO tool implements the metrics for all options, namely for unweighted graphs, weighted graphs, unweighted digraphs and weighted digraphs, it is also useful for network analysis into other areas beyond team sports. The book assists the reader to compute the metrics and to use it in different scenarios.
Автор: Krej?? Название: Pairwise Comparison Matrices and their Fuzzy Extension ISBN: 3319777149 ISBN-13(EAN): 9783319777146 Издательство: Springer Рейтинг: Цена: 23757.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book offers the first comprehensive and critical literature review of fuzzy pairwise comparison methods derived from methods originally developed for crisp pairwise comparison matrices. It proposes new fuzzy extensions of these methods and provides a detailed study of the differences and analogies between all the reviewed methods, as well as a detailed description of their drawbacks, with the help of many numerical examples. In order to prevent the drawbacks related to the reviewed fuzzy pairwise comparison methods, the book introduces constrained fuzzy arithmetic in fuzzy extension of the pairwise comparison methods. It proposes new fuzzy pairwise comparison methods based on constrained fuzzy arithmetic and critically compares them with the reviewed methods. It describes the application of the newly developed methods to incomplete large-dimensional pairwise comparison matrices showcased in a real-life case study. Written for researchers, graduate and PhD students interested in multi-criteria decision making methods based on both crisp and fuzzy pairwise comparison matrices, this self-contained book offers an overview of cutting-edge research and all necessary information to understand the described tools and use them in real-world applications.
Автор: Krassimir T. Atanassov Название: Index Matrices: Towards an Augmented Matrix Calculus ISBN: 3319365045 ISBN-13(EAN): 9783319365046 Издательство: Springer Рейтинг: Цена: 13059.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents the very concept of an index matrix and its related augmented matrix calculus in a comprehensive form. It mostly illustrates the exposition with examples related to the generalized nets and intuitionistic fuzzy sets which are examples of an extremely wide array of possible application areas.
Автор: Martin Gavalec; Jaroslav Ram?k; Karel Zimmermann Название: Decision Making and Optimization ISBN: 3319083228 ISBN-13(EAN): 9783319083223 Издательство: Springer Рейтинг: Цена: 9781.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The book is a benefit for graduate and postgraduate students in the areas of operations research, decision theory, optimization theory, linear algebra, interval analysis and fuzzy sets.
Автор: Aaart R. Heesterman Название: Matrices and Simplex Algorithms ISBN: 9400979436 ISBN-13(EAN): 9789400979437 Издательство: Springer Рейтинг: Цена: 11173.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This is a textbook devoted to mathematical programming algorithms and the mathematics needed to understand such algorithms. It is a textbook as well a~ in parts, a contribution to new knowledge. Part II is mainly devoted to linear programming. viii INTRODUCTION Parts III and IV are concerned with nonlinear programming.
Автор: Jana Krej?? Название: Pairwise Comparison Matrices and their Fuzzy Extension ISBN: 3030085198 ISBN-13(EAN): 9783030085193 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book offers the first comprehensive and critical literature review of fuzzy pairwise comparison methods derived from methods originally developed for crisp pairwise comparison matrices. It proposes new fuzzy extensions of these methods and provides a detailed study of the differences and analogies between all the reviewed methods, as well as a detailed description of their drawbacks, with the help of many numerical examples. In order to prevent the drawbacks related to the reviewed fuzzy pairwise comparison methods, the book introduces constrained fuzzy arithmetic in fuzzy extension of the pairwise comparison methods. It proposes new fuzzy pairwise comparison methods based on constrained fuzzy arithmetic and critically compares them with the reviewed methods. It describes the application of the newly developed methods to incomplete large-dimensional pairwise comparison matrices showcased in a real-life case study. Written for researchers, graduate and PhD students interested in multi-criteria decision making methods based on both crisp and fuzzy pairwise comparison matrices, this self-contained book offers an overview of cutting-edge research and all necessary information to understand the described tools and use them in real-world applications.
Автор: Wu Название: Robust Latent Feature Learning for Incomplete Big Data ISBN: 9811981396 ISBN-13(EAN): 9789811981395 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Incomplete big data are frequently encountered in many industrial applications, such as recommender systems, the Internet of Things, intelligent transportation, cloud computing, and so on. It is of great significance to analyze them for mining rich and valuable knowledge and patterns. Latent feature analysis (LFA) is one of the most popular representation learning methods tailored for incomplete big data due to its high accuracy, computational efficiency, and ease of scalability. The crux of analyzing incomplete big data lies in addressing the uncertainty problem caused by their incomplete characteristics. However, existing LFA methods do not fully consider such uncertainty. In this book, the author introduces several robust latent feature learning methods to address such uncertainty for effectively and efficiently analyzing incomplete big data, including robust latent feature learning based on smooth L1-norm, improving robustness of latent feature learning using L1-norm, improving robustness of latent feature learning using double-space, data-characteristic-aware latent feature learning, posterior-neighborhood-regularized latent feature learning, and generalized deep latent feature learning. Readers can obtain an overview of the challenges of analyzing incomplete big data and how to employ latent feature learning to build a robust model to analyze incomplete big data. In addition, this book provides several algorithms and real application cases, which can help students, researchers, and professionals easily build their models to analyze incomplete big data.
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