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Vladik Kreinovich - From Intervals to –? : Towards a General Description of Validated Uncertainty

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Vladik Kreinovich From Intervals to –? : Towards a General Description of Validated Uncertainty

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From Intervals to (2023) [Kreinovich et al] [9783031205682]

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Book cover of From Intervals to Volume 1041 Studies in Computational - photo 1
Book cover of From Intervals to ?
Volume 1041
Studies in Computational Intelligence
Series Editor
Janusz Kacprzyk
Polish Academy of Sciences, Warsaw, Poland

The series Studies in Computational Intelligence (SCI) publishes new developments and advances in the various areas of computational intelligencequickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life sciences, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output.

Indexed by SCOPUS, DBLP, WTI Frankfurt eG, zbMATH, SCImago.

All books published in the series are submitted for consideration in Web of Science.

Vladik Kreinovich , Graaliz Pereira Dimuro and Antnio Carlos da Rocha Costa
From Intervals to ?
Towards a General Description of Validated Uncertainty
Logo of the publisher Vladik Kreinovich Department of Computer Science - photo 2
Logo of the publisher
Vladik Kreinovich
Department of Computer Science, University of Texas at El Paso, El Paso, TX, USA
Graaliz Pereira Dimuro
Centro de Cincias Computacionais, Federal University of Rio Grande, Rio Grande, Rio Grande do Sul, Brazil
Universidad Publica de NavarraUPNA, Pamplona, Spain
Antnio Carlos da Rocha Costa
Programa de Ps-Graduao em Filosofia, Pontifcia Universidade Catlica do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil
ISSN 1860-949X e-ISSN 1860-9503
Studies in Computational Intelligence
ISBN 978-3-031-20568-2 e-ISBN 978-3-031-20569-9
https://doi.org/10.1007/978-3-031-20569-9
The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed.
The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.
The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This Springer imprint is published by the registered company Springer Nature Switzerland AG

The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

In many real-life situations, we are interested in the physical quantities that are difficult or even impossible to measure directly. To estimate the value of such quantity we measure the values of auxiliary quantities that are related to - photo 3 , we measure the values of auxiliary quantities that are related to by a known functional relation an - photo 4 that are related to From Intervals to Towards a General Description of Validated Uncertainty - image 5 by a known functional relation From Intervals to Towards a General Description of Validated Uncertainty - image 6 , and we then use the results From Intervals to Towards a General Description of Validated Uncertainty - image 7 of measuring From Intervals to Towards a General Description of Validated Uncertainty - image 8 to find the desired estimate From Intervals to Towards a General Description of Validated Uncertainty - image 9 . Due to measurement errors, the measured values From Intervals to Towards a General Description of Validated Uncertainty - image 10 are slightly different from the actual (unknown) values From Intervals to Towards a General Description of Validated Uncertainty - image 11 . In other words, we have a nonzero measurement errors From Intervals to Towards a General Description of Validated Uncertainty - image 12 . As a result, our estimate From Intervals to Towards a General Description of Validated Uncertainty - image 13 is different from the actual value of the desired quantity When a measured quantity is described by a number - photo 14 of the desired quantity.

When a measured quantity From Intervals to Towards a General Description of Validated Uncertainty - image 15 is described by a number, then we usually know the upper bound From Intervals to Towards a General Description of Validated Uncertainty - image 16 on the absolute value of the measurement error. In this case, after we get the measurement result From Intervals to Towards a General Description of Validated Uncertainty - image 17

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