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001 978-3-319-26718-0
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008 151208s2016 sz | s |||| 0|eng d
020 _a9783319267180
_9978-3-319-26718-0
024 7 _a10.1007/978-3-319-26718-0
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aTEC009000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aBiswas, Ranjit.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_941795
245 1 0 _aIs ‘Fuzzy Theory’ an Appropriate Tool for Large Size Problems?
_h[electronic resource] /
_cby Ranjit Biswas.
250 _a1st ed. 2016.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aVIII, 64 p. 17 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Computational Intelligence,
_x2625-3712
505 0 _aTwo 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 _aThe 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.
650 0 _aComputational intelligence.
_97716
650 0 _aArtificial intelligence.
_93407
650 1 4 _aComputational Intelligence.
_97716
650 2 4 _aArtificial Intelligence.
_93407
710 2 _aSpringerLink (Online service)
_941796
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319267173
776 0 8 _iPrinted edition:
_z9783319267197
830 0 _aSpringerBriefs in Computational Intelligence,
_x2625-3712
_941797
856 4 0 _uhttps://doi.org/10.1007/978-3-319-26718-0
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c77009
_d77009