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001 978-94-91216-28-2
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005 20160302170415.0
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008 120301s2010 fr | s |||| 0|eng d
020 _a9789491216282
_9978-94-91216-28-2
024 7 _a10.2991/978-94-91216-28-2
_2doi
050 4 _aP98-98.5
072 7 _aUYQL
_2bicssc
072 7 _aCOM042000
_2bisacsh
082 0 4 _a006.35
_223
100 1 _aPei, Zheng.
_eauthor.
245 1 0 _aLinguistic Values Based Intelligent Information Processing: Theory, Methods, and Applications
_h[electronic resource] /
_cby Zheng Pei, Da Ruan, Jun Liu, Yang Xu.
264 1 _aParis :
_bAtlantis Press,
_c2010.
300 _aXX, 276 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aAtlantis Computational Intelligence Systems,
_x1875-7650 ;
_v1
505 0 _aThe 2-Tuple Fuzzy Linguistic Representation Model -- Hedge Algebras of Linguistic Values -- Linguistic Information Processing Based on Lattice Structrure -- Fuzzy Number Indexes of Linguistic Values -- Hierarchical Structure Analysis of Linguistic Values -- Conclusions and RelatedWorks.
520 _aHumans employ mostly natural languages in describing and representing problems, c- puting and reasoning, arriving at ?nal conclusions described similarly as words in a natural language or as the form of mental perceptions. To make machines imitate humans’ mental activities, the key point in terms of machine intelligence is to process uncertain information by means of natural languages with vague and imprecise concepts. Zadeh (1996a) proposed a concept of Computing with Words (CWW) to model and c- pute with linguistic descriptions that are propositions drawn from a natural language. CWW, followed the concept of linguistic variables (Zadeh, 1975a,b) and fuzzy sets (Zadeh, 1965), has been developed intensively and opened several new vast research ?elds as well as applied in various areas, particularly in the area of arti?cial intelligence. Zadeh (1997, 2005) emphasized that the core conceptions in CWW are linguistic variables and fuzzy logic (or approximate reasoning). In a linguistic variable, each linguistic value is explained by a fuzzy set (also called semantics of the linguistic value), its membership function is de?ned on the universe of discourse of the linguistic variable. By fuzzy sets, linguistic information or statements are quanti?ed by membership functions, and infor- tion propagation is performed by approximate reasoning. The use of linguistic variables implies processes of CWW such as their fusion, aggregation, and comparison. Different computational approaches in the literature addressed those processes (Wang, 2001; Zadeh and Kacprzyk, 1999a, b). Membership functions are generally at the core of many fuzzy-set theories based CWW.
650 0 _aComputer science.
650 0 _aComputational linguistics.
650 1 4 _aComputer Science.
650 2 4 _aLanguage Translation and Linguistics.
700 1 _aRuan, Da.
_eauthor.
700 1 _aLiu, Jun.
_eauthor.
700 1 _aXu, Yang.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
830 0 _aAtlantis Computational Intelligence Systems,
_x1875-7650 ;
_v1
856 4 0 _uhttp://dx.doi.org/10.2991/978-94-91216-28-2
912 _aZDB-2-SCS
999 _c195183
_d195183