Is ‘Fuzzy Theory’ an Appropriate Tool for Large Size Problems? (Record no. 77009)

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fixed length control field 04034nam a22005055i 4500
001 - CONTROL NUMBER
control field 978-3-319-26718-0
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220801215017.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 151208s2016 sz | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783319267180
-- 978-3-319-26718-0
082 04 - CLASSIFICATION NUMBER
Call Number 006.3
100 1# - AUTHOR NAME
Author Biswas, Ranjit.
245 10 - TITLE STATEMENT
Title Is ‘Fuzzy Theory’ an Appropriate Tool for Large Size Problems?
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2016.
300 ## - PHYSICAL DESCRIPTION
Number of Pages VIII, 64 p. 17 illus. in color.
490 1# - SERIES STATEMENT
Series statement SpringerBriefs in Computational Intelligence,
505 0# - FORMATTED CONTENTS NOTE
Remark 2 Two Hidden Facts about Fuzzy Set Theory (and, about any Soft Computing Set Theory) -- Cognitive Intuitionistic Fuzzy System (CIFS) -- Is „Fuzzy Theory‟ an Appropriate Tool for Large Size Problems? -- Ordering (or Ranking) of Elements in an IFS on the basis of Their Amount of Belongingness -- An Application Domain to Understand the Potential of Intuitionistic Fuzzy Theory over Fuzzy Theory -- An Example of Application Domain to Understand the Potential of Fuzzy Theory over Intuitionistic Fuzzy Theory in Some Cases -- Conclusion -- Future Research Directions.
520 ## - SUMMARY, ETC.
Summary, etc The work in this book is based on philosophical as well as logical views on the subject of decoding the ‘progress’ of decision making process in the cognition system of a decision maker (be it a human or an animal or a bird or any living thing which has a brain) while evaluating the membership value µ(x) in a fuzzy set or in an intuitionistic fuzzy set or in any such soft computing set model or in a crisp set. A new theory is introduced called by “Theory of CIFS”. The following two hypothesis are hidden facts in fuzzy computing or in any soft computing process :- Fact-1: A decision maker (intelligent agent) can never use or apply ‘fuzzy theory’ or any soft-computing set theory without intuitionistic fuzzy system. Fact-2 : The Fact-1 does not necessarily require that a fuzzy decision maker (or a crisp ordinary decision maker or a decision maker with any other soft theory models or a decision maker like animal/bird which has brain, etc.) must be aware or knowledgeable about IFS Theory! The “Theory of CIFS” is developed with a careful analysis unearthing the correctness of these two facts. Two examples of ‘decision making problems’ with complete solutions are presented out of which one example will show the dominance of the application potential of intuitionistic fuzzy set theory over fuzzy set theory, and the other will show the converse i.e. the dominance of the application potential of fuzzy set theory over intuitionistic fuzzy set theory in some cases. The “Theory of CIFS” may be viewed to belong to the subjects : Theory of Intuitionistic Fuzzy Sets, Soft Computing, Artificial Intelligence, etc.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://doi.org/10.1007/978-3-319-26718-0
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
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-- Springer International Publishing :
-- Imprint: Springer,
-- 2016.
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-- computer
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-- online resource
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-- text file
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650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computational intelligence.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Artificial intelligence.
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computational Intelligence.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Artificial Intelligence.
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
-- 2625-3712
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-- ZDB-2-ENG
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