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Beyond traditional probabilistic dat...

Kosheleva, Olga.

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Beyond traditional probabilistic data processing techniques = interval, fuzzy etc. methods and their applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Beyond traditional probabilistic data processing techniques/ edited by Olga Kosheleva ... [et al.].
其他題名:	interval, fuzzy etc. methods and their applications /
其他作者:	Kosheleva, Olga.
出版者:	Cham :Springer International Publishing : : 2020.,
面頁冊數:	xi, 649 p. :ill. (some col.), digital ;24 cm.
內容註:	Symmetries are Important -- Constructive Continuity of Increasing Functions -- A Constructive Framework for Teaching Discrete Mathematics -- Fuzzy Logic for Incidence Geometry -- Strengths of Fuzzy Techniques in Data Science -- Impact of Time Delays on Networked Control of Autonomous Systems -- Sets and Systems -- An Overview of Polynomially Computable Characteristics of Special Interval Matrices -- Interval Regularization for Inaccurate Linear Algebraic Equations -- Measurable Process Selection Theorem and Non-Autonomous Inclusions -- Handling Uncertainty When Getting Contradictory Advice from Experts -- Why Sparse? -- The Kreinovich Temporal Universe -- Integral Transforms induced by Heaviside Perceptrons.
Contained By:	Springer eBooks
標題:	Fuzzy mathematics. -
電子資源:	https://doi.org/10.1007/978-3-030-31041-7
ISBN:	9783030310417

Beyond traditional probabilistic data processing techniques = interval, fuzzy etc. methods and their applications /
Beyond traditional probabilistic data processing techniquesinterval, fuzzy etc. methods and their applications /[electronic resource] :edited by Olga Kosheleva ... [et al.]. - Cham :Springer International Publishing :2020. - xi, 649 p. :ill. (some col.), digital ;24 cm. - Studies in computational intelligence,8351860-949X ;. - Studies in computational intelligence ;835..

Symmetries are Important -- Constructive Continuity of Increasing Functions -- A Constructive Framework for Teaching Discrete Mathematics -- Fuzzy Logic for Incidence Geometry -- Strengths of Fuzzy Techniques in Data Science -- Impact of Time Delays on Networked Control of Autonomous Systems -- Sets and Systems -- An Overview of Polynomially Computable Characteristics of Special Interval Matrices -- Interval Regularization for Inaccurate Linear Algebraic Equations -- Measurable Process Selection Theorem and Non-Autonomous Inclusions -- Handling Uncertainty When Getting Contradictory Advice from Experts -- Why Sparse? -- The Kreinovich Temporal Universe -- Integral Transforms induced by Heaviside Perceptrons.

Data processing has become essential to modern civilization. The original data for this processing comes from measurements or from experts, and both sources are subject to uncertainty. Traditionally, probabilistic methods have been used to process uncertainty. However, in many practical situations, we do not know the corresponding probabilities: in measurements, we often only know the upper bound on the measurement errors; this is known as interval uncertainty. In turn, expert estimates often include imprecise (fuzzy) words from natural language such as "small"; this is known as fuzzy uncertainty. In this book, leading specialists on interval, fuzzy, probabilistic uncertainty and their combination describe state-of-the-art developments in their research areas. 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.

ISBN: 9783030310417

Standard No.: 10.1007/978-3-030-31041-7doiSubjects--Topical Terms:

582139
Fuzzy mathematics.

LC Class. No.: QA248.5 / .B496 2020

Dewey Class. No.: 519.5

Beyond traditional probabilistic data processing techniques = interval, fuzzy etc. methods and their applications /
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520 $a Data processing has become essential to modern civilization. The original data for this processing comes from measurements or from experts, and both sources are subject to uncertainty. Traditionally, probabilistic methods have been used to process uncertainty. However, in many practical situations, we do not know the corresponding probabilities: in measurements, we often only know the upper bound on the measurement errors; this is known as interval uncertainty. In turn, expert estimates often include imprecise (fuzzy) words from natural language such as "small"; this is known as fuzzy uncertainty. In this book, leading specialists on interval, fuzzy, probabilistic uncertainty and their combination describe state-of-the-art developments in their research areas. 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.
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