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dbpedia:George_Klir dbpedia-owl:knownFor dbpedia:Fuzzy_logic .
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dbpedia:George_Klir ns2:knownFor dbpedia:Fuzzy_logic .
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dbpedia:George_Klir dbpprop:knownFor dbpedia:Fuzzy_logic .
dbpedia:Lotfi_Asker_Zadeh dbpedia-owl:knownFor dbpedia:Fuzzy_logic ;
ns2:knownFor dbpedia:Fuzzy_logic ;
dbpprop:knownFor dbpedia:Fuzzy_logic .
dbpedia:Fuzzy_Logic dbpprop:redirect dbpedia:Fuzzy_logic .
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dbpedia:Fuzzy_logic owl:sameAs ns5:Mx4rvzQKAJwpEbGdrcN5Y29ycA ,
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dbpedia:Fuzzy_logic foaf:page ns7:Fuzzy_logic ;
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dbpedia:Fuzzy_logic dbpprop:reference ns8:Modeling_with_words ,
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dbpedia:Fuzzy_logic dbpprop:reference ns9:dotfuzzy ,
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dbpedia:Fuzzy_logic dbpprop:reference ns10:WEB_3 ,
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dbpedia:Fuzzy_logic dbpprop:reference ns11:Formal_fuzzy_logic ,
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dbpedia:Fuzzy_logic dbpprop:reference ns12:pyfuzzylib ,
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dbpedia:Fuzzy_logic dbpprop:reference ns13:rockonfuzzy .
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dbpedia:Fuzzy_logic dbpprop:reference ns14:fuzzy-math-part-1-the-theory ,
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dbpedia:Fuzzy_logic rdfs:label "\u041D\u0435\u0447\u0456\u0442\u043A\u0430 \u043B\u043E\u0433\u0456\u043A\u0430"@uk ,
"Suddig logik"@sv ,
"Elmos\u00F3dott halmazok logik\u00E1ja"@hu ,
"Logika rozmyta"@pl ,
"L\u00F3gica difusa"@pt ,
"Fuzzylogikk"@no ,
"Fuzzylogik"@de ,
"\u30D5\u30A1\u30B8\u30A3\u8AD6\u7406"@ja ,
"L\u00F3gica difusa"@es ,
"Fuzzy logic"@nl ,
"Fuzzy logika"@cs ,
"Logica fuzzy"@ro ,
"Logica fuzzy"@it ,
"Fuzzy logic"@en ,
"Bulan\u0131k mant\u0131k"@tr ,
"Logique floue"@fr ,
"\u6A21\u7CCA\u903B\u8F91"@zh ,
"Sumea logiikka"@fi ,
"\u041D\u0435\u0447\u0451\u0442\u043A\u0430\u044F \u043B\u043E\u0433\u0438\u043A\u0430"@ru ;
dbpprop:abstract "\u041D\u0435\u0447\u0456\u0442\u043A\u0430 \u043B\u043E\u0433\u0456\u043A\u0430 (\u0432\u0456\u0434 \u0430\u043D\u0433\u043B. fuzzy logic) \u044F\u043A \u043D\u0430\u0443\u043A\u0430 \u0431\u0443\u043B\u0430 \u0437\u0430\u043F\u043E\u0447\u0430\u0442\u043A\u043E\u0432\u0430\u043D\u0430 \u0430\u043C\u0435\u0440\u0438\u043A\u0430\u043D\u0441\u044C\u043A\u0438\u043C \u0432\u0447\u0435\u043D\u0438\u043C \u0456\u0440\u0430\u043D\u0441\u044C\u043A\u043E\u0433\u043E \u043F\u043E\u0445\u043E\u0434\u0436\u0435\u043D\u043D\u044F \u041B\u043E\u0442\u0444\u0456 \u0410. \u0417\u0430\u0434\u0435 (Lotfi A. Zadeh). \u041D\u0430 \u0432\u0456\u0434\u043C\u0456\u043D\u0443 \u0432\u0456\u0434 \u0431\u0443\u043B\u0435\u0432\u043E\u0457 \u0430\u043B\u0433\u0435\u0431\u0440\u0438, \u0443 \u043A\u043E\u0442\u0440\u0456\u0439 \u0456\u0441\u043D\u0443\u0454 \u043B\u0438\u0448\u0435 \u0434\u0432\u0456 \u0432\u0435\u043B\u0438\u0447\u0438\u043D\u0438 (0 \u0442\u0430 1, \u043F\u0440\u0430\u0432\u0434\u0430 \u0447\u0438 \u043D\u0435\u043F\u0440\u0430\u0432\u0434\u0430) \u0443 \u043D\u0435\u0447\u0456\u0442\u043A\u0456\u0439 \u043B\u043E\u0433\u0456\u0446\u0456 \u0456\u0441\u043D\u0443\u044E\u0442\u044C \u0442\u0430\u043A\u043E\u0436 \u043F\u0435\u0440\u0435\u0445\u0456\u0434\u043D\u0456 \u0432\u0435\u043B\u0438\u0447\u0438\u043D\u0438 (\u0441\u0442\u0430\u043D\u0438). \u041E\u0434\u043D\u0456 \u0437 \u043E\u0441\u043D\u043E\u0432\u043D\u0438\u0445 \u043F\u043E\u043D\u044F\u0442\u044C \u043D\u0435\u0447\u0456\u0442\u043A\u043E\u0457 \u043B\u043E\u0433\u0456\u043A\u0438: \u0444\u0430\u0437\u0456-\u043C\u043D\u043E\u0436\u0438\u043D\u0438, \u0444\u0430\u0437\u0456\u0444\u0456\u043A\u0430\u0446\u0456\u044F/\u0434\u0435\u0444\u0430\u0437\u0456\u0444\u0456\u043A\u0430\u0446\u0456\u044F, \u0444\u0430\u0437\u0456-\u043E\u043F\u0435\u0440\u0430\u0446\u0456\u044F. \u0412 \u043E\u0441\u0442\u0430\u043D\u043D\u0456 \u0440\u043E\u043A\u0438 \u0437\u043D\u0430\u0447\u0435\u043D\u043D\u044F \u043D\u0435\u0447\u0456\u0442\u043A\u043E\u0457 \u043B\u043E\u0433\u0456\u043A\u0438 \u0441\u0442\u0440\u0456\u043C\u043A\u043E \u0432\u0438\u0440\u043E\u0441\u043B\u043E \u0443 \u0441\u0432\u0456\u0442\u0456 \u0432\u0438\u0441\u043E\u043A\u0438\u0445 \u0442\u0435\u0445\u043D\u043E\u043B\u043E\u0433\u0456\u0439."@uk ,
"Fuzzylogikk inneb\u00E6rer at man baserer avgj\u00F8relser p\u00E5 'godt nok' eller 'n\u00E6r nok' vurderinger. Det finnes mange former for Fuzzylogikk og mange ulike algoritmer for \u00E5 beregne n\u00E6rhet. Et eksempel er innen navnematching. En person som heter Petter Hansen kan feilaktig registreres som Peter Hansen eller Petter Hanssen. Dermed kan dubletter i registere oppst\u00E5, og man f\u00E5r behov for \u00E5 sl\u00E5 sammen variasjoner av ett individ. N\u00E5 er kun navn en svak indikasjon alene, men om man vet feks alder og adresse ogs\u00E5 \u00F8ker sannsynligheten for match. I Fuzzylogikk ville man feks kunne sl\u00E5 sammen dobbeltregistreringer p\u00E5 personer med samme eller nesten samme navn, gitt at alder og adresse eller nesten samme adresse stemmer."@no ,
"Fuzzy logic (soms vage logica of wollige logica genoemd) is een stroming binnen de logica. Zij kan gezien worden als een uitbreiding van Booleaanse (boolean) logica. Het principe uit de Booleaanse logica dat iets of waar of onwaar is, wordt losgelaten; het is dus een vorm van meerwaardige logica. In plaats daarvan worden er waarheidswaarden gebruikt tussen 0 (onwaar) en 1 (waar) in. Het discrete karakter van de traditionele logica wordt hiermee ook losgelaten, iets kan bijvoorbeeld voor 1/3 waar zijn. Of 'een beetje' waar. Het Engelse woord fuzzy betekent wazig, wollig. De grondlegger van de fuzzy logic is Lotfi A. Zadeh. Hij ontwikkelde deze logica om met onzekerheden die in natuurlijke taal voorkomen te kunnen omgaan met formele middelen. Het idee valt uit te leggen aan de hand van een voorbeeld. Het woord 'lang' komt in onze natuurlijke taal voor en zegt iets over de grootte van iets. We kennen echter geen harde grenzen over wanneer iets nu precies 'lang' is. Neem bijvoorbeeld de bewering \"Jan is lang. \" Volgens de booleaanse logica zou deze zin waar of onwaar moeten zijn. Wanneer Jan \u00E9\u00E9n meter zestig is, kunnen we wel stellen dat de zin onwaar is en wanneer Jan twee meter is, zal niemand bestrijden dat de bewering waar is. Maar wat zeggen we wanneer Jan \u00E9\u00E9n meter tachtig is? De ene persoon zal dit lang noemen, terwijl de ander het niet zo lang vindt. Met fuzzy logic kan dan aangegeven worden dat Jan enigszins lang is, door te stellen dat de bewering voor 0,6 waar is. Fuzzy logic vindt een praktische toepassing in de meet- en regeltechniek, waar het soms ook 'Fuzzy control' genoemd wordt. Met regels als \"zet de verwarming aan als het koud is\" kan hiermee op eenvoudige manier een temperatuurregeling beschreven en ook ge\u00EFmplementeerd worden. Bij goede definitie van \"aanzetten\" en \"koud\" kan hiermee een constantere temperatuur bereikt worden dan met een simpele 'aan-uit' regeling, omdat de verwarming dan maar een beetje aangaat als het een beetje koud is. Bij de aankoop van een wasmachine doelt \"Fuzzy logic\" op de functie dat de wasmachine zelf de hoeveelheid was 'meet' en het waterverbruik hieraan aanpast."@nl ,
"Az elmos\u00F3dott halmazok logik\u00E1ja a t\u00F6bb\u00E9rt\u00E9k\u0171 logikai szemantik\u00E1k egyike. Tulajdonk\u00E9ppen fuzzy logika n\u00E9v alatt egy eg\u00E9sz elm\u00E9letcsal\u00E1dr\u00F3l besz\u00E9lhet\u00FCnk, melynek sokr\u00E9t\u0171 alkalmaz\u00E1sai vannak els\u0151sorban az informatik\u00E1ban, de alkalmaz\u00E1sra tal\u00E1lt a nyelvtudom\u00E1nyi \u00E9s logikai szemantik\u00E1ban, a matematikai logik\u00E1ban \u00E9s a val\u00F3sz\u00EDn\u0171s\u00E9gelm\u00E9letben is. A t\u00E1gabb \u00E9rtelemben vett fuzzy logika alapj\u00E1t k\u00E9pezi a fuzzy sz\u00E1m\u00EDt\u00F3g\u00E9pes rendszereknek, melyek szemben a szokv\u00E1nyos rendszerekkel, nem csak igen \u00E9s nem (illetve ki \u00E9s be, vagy 1 \u00E9s 0) \u00E9rt\u00E9kekkel dolgoznak, hanem k\u00F6zb\u00FCls\u0151 \u201Eval\u00F3s\u00E1g\u00E9rt\u00E9kekkel\u201D is, mint p\u00E9ld\u00E1ul 0,5 (f\u00E9ligmeddig), 0,2 (kicsit), 0,8 (el\u00E9gg\u00E9)\u2026 Ez\u00E1ltal az \u201E\u00E9letlen\u201D (fuzzy) meghat\u00E1roz\u00E1sok (mint p\u00E9ld\u00E1ul az el\u0151bbiek) matematikailag kezelhet\u0151v\u00E9 v\u00E1lnak. Manaps\u00E1g a fuzzy logika illetve a fuzzy-control, teh\u00E1t a fuzzy logik\u00E1n alapul\u00F3 ir\u00E1ny\u00EDt\u00E1s, els\u0151sorban g\u00E9pek \u00E9s robotok, h\u00E1ztart\u00E1si k\u00E9sz\u00FCl\u00E9kek ir\u00E1ny\u00EDt\u00E1s\u00E1ban tal\u00E1l alkalmaz\u00E1sra."@hu ,
"La l\u00F3gica borrosa o difusa se basa en lo relativo de lo observado. Este tipo de l\u00F3gica toma dos valores aleatorios, pero contextualizados y referidos entre s\u00ED. As\u00ED, por ejemplo, una persona que mida 2 metros es claramente una persona alta, si previamente se ha tomado el valor de persona baja y se ha establecido en 1 metro. Ambos valores est\u00E1n contextualizados a personas y referidos a una medida m\u00E9trica lineal."@es ,
"Bulan\u0131k mant\u0131k, 1961 y\u0131l\u0131nda L\u00FCtfi Askerzade'nin yay\u0131nlad\u0131\u011F\u0131 bir makalenin sonucu olu\u015Fmu\u015F bir mant\u0131k yap\u0131s\u0131d\u0131r. Bulan\u0131k mant\u0131\u011F\u0131n temeli bulan\u0131k k\u00FCme ve alt k\u00FCmelere dayan\u0131r. Klasik yakla\u015F\u0131mda bir varl\u0131k ya k\u00FCmenin eleman\u0131d\u0131r ya da de\u011Fildir. Matematiksel olarak ifade edildi\u011Finde varl\u0131k k\u00FCme ile olan \u00FCyelik ili\u015Fkisi bak\u0131m\u0131ndan k\u00FCmenin eleman\u0131 oldu\u011Funda \"1\", k\u00FCmenin eleman\u0131 olmad\u0131\u011F\u0131 zaman \"0\" de\u011Ferini al\u0131r. Bulan\u0131k mant\u0131k klasik k\u00FCme g\u00F6steriminin geni\u015Fletilmesidir. Bulan\u0131k varl\u0131k k\u00FCmesinde her bir varl\u0131\u011F\u0131n \u00FCyelik derecesi vard\u0131r. Varl\u0131klar\u0131n \u00FCyelik derecesi, (0, 1) aral\u0131\u011F\u0131nda herhangi bir de\u011Fer olabilir ve \u00FCyelik fonksiyonu M(x) ile g\u00F6sterilir . \u00D6rnek olarak normal oda s\u0131cakl\u0131\u011F\u0131n\u0131 23 derece olarak kabul edersek klasik k\u00FCme kuram\u0131na g\u00F6re 23 derecenin \u00FCzerindeki s\u0131cakl\u0131k derecelerini s\u0131cak olarak kabul ederiz ve bu derecelerin s\u0131cak k\u00FCmesindeki \u00FCyelik dereceleri \"1\" olur. 23 alt\u0131ndaki s\u0131cakl\u0131k dereceleri ise so\u011Fuktur ve s\u0131cak k\u00FCmesindeki \u00FCyelik dereceleri \"0\" olur. So\u011Fuk k\u00FCmesini temel ald\u0131\u011F\u0131m\u0131zda bu de\u011Ferler tersine d\u00F6ner. Bulan\u0131k k\u00FCme yakla\u015F\u0131m\u0131nda \u00FCyelik de\u011Ferleri [0,1] aral\u0131\u011F\u0131nda de\u011Ferler almaktad\u0131r. \u00D6rne\u011Fin 14 derecelik s\u0131cakl\u0131k i\u00E7in \u00FCyelik derecesi \"0\", 23 s\u0131cakl\u0131k derecesi i\u00E7in \u00FCyelik de\u011Feri \"0,25\" olabilir. \u201Cdo\u011Fru\u201D, \u201D\u00E7ok do\u011Fru\u201D, \u201Daz \u00E7ok do\u011Fru\u201D v.b. gibi s\u00F6zel olarak ifade edilen (linguistik-dilsel-de\u011Fi\u015Fkenli)do\u011Fruluk derecelerine sahip olmas\u0131, Ge\u00E7erlili\u011Fi kesin de\u011Fil fakat yakla\u015F\u0131k olan \u00E7\u0131kar\u0131m kurallar\u0131na sahip olmas\u0131, Her kavram\u0131n bir derecesi olmas\u0131, Her mant\u0131ksal sistemin bulan\u0131kla\u015Ft\u0131r\u0131labilmesi, Bulan\u0131k mant\u0131kta bilginin, bulan\u0131k k\u0131s\u0131tlara ait de\u011Fi\u015Fkenlerin esnekli\u011Fi veya denkli\u011Fiyle yorumlanmas\u0131. Klasik k\u00FCmelerin aksine bulan\u0131k k\u00FCmelerde elemanlar\u0131n \u00FCyelik dereceleri [0, 1] aral\u0131\u011F\u0131nda sonsuz say\u0131da de\u011Fi\u015Febilir. Bunlar \u00FCyeli\u011Fin derecelerinin devaml\u0131 ve aral\u0131ks\u0131z b\u00FCt\u00FCn\u00FCyle bir k\u00FCmedir. Keskin k\u00FCmelerdeki so\u011Fuk-s\u0131cak, h\u0131zl\u0131-yava\u015F, ayd\u0131nl\u0131k-karanl\u0131k gibi ikili de\u011Fi\u015Fkenler, bulan\u0131k mant\u0131kta biraz so\u011Fuk, biraz s\u0131cak, biraz karanl\u0131k gibi esnek niteleyicilerle yumu\u015Fat\u0131larak ger\u00E7ek d\u00FCnyaya benzetilir. En \u00F6nemli fark, b\u00F6yle bir \u00E7at\u0131da bilginin kayna\u011F\u0131ndaki k\u00FCme \u00FCyeli\u011Finin kesin tan\u0131mlanm\u0131\u015F \u00F6nko\u015Fullar\u0131n\u0131n olmay\u0131\u015F\u0131 ve daha \u00E7ok problemlerle rasgele de\u011Fi\u015Fkenlerin haz\u0131r bulunmas\u0131ndad\u0131r. Bir \u015Feyin varl\u0131\u011F\u0131 kendisine ait bir isimle do\u011Far. Evrendekilerin tamam\u0131 hem (ya) tek (1) hem de (ya da) sonsuz eksi tektir (sonsuz -1). Klasik mant\u0131k ile bulan\u0131k mant\u0131k aras\u0131ndaki temel farkl\u0131l\u0131klar : ! Klasik Mant\u0131k ! Bulan\u0131k Mant\u0131k | A veya A De\u011Fil | A ve A De\u011Fil | Kesin | K\u0131smi | Hepsi veya Hi\u00E7biri | Belirli Derecelerde | 0 veya 1 | 0 ve 1 Aras\u0131nda S\u00FCreklilik | \u0130kili Birimler | Bulan\u0131k Birimler"@tr ,
"\u6A21\u7CCA\u903B\u8F91\u662F\u5904\u7406\u90E8\u5206\u771F\u5B9E\u6982\u5FF5\u7684\u5E03\u5C14\u903B\u8F91\u6269\u5C55\u3002\u7ECF\u5178\u903B\u8F91\u575A\u6301\u6240\u6709\u4E8B\u7269(\u9648\u8FF0)\u90FD\u53EF\u4EE5\u7528\u4E8C\u5143\u9879(0 \u6216 1\uFF0C\u9ED1\u6216\u767D\uFF0C\u662F\u6216\u5426)\u6765\u8868\u8FBE\uFF0C\u800C\u6A21\u7CCA\u903B\u8F91\u7528\u771F\u5B9E\u5EA6\u66FF\u4EE3\u4E86\u5E03\u5C14\u771F\u503C\u3002\u8FD9\u4E9B\u9648\u8FF0\u8868\u793A\u5B9E\u9645\u4E0A\u63A5\u8FD1\u4E8E\u65E5\u5E38\u4EBA\u4EEC\u7684\u95EE\u9898\u548C\u8A9E\u610F\u9648\u8FF0\uFF0C\u56E0\u4E3A\u201C\u771F\u5B9E\u201D\u548C\u7ED3\u679C\u5728\u591A\u6570\u65F6\u5019\u662F\u90E8\u5206(\u975E\u4E8C\u5143)\u7684\u548C/\u6216\u4E0D\u7CBE\u786E\u7684(\u4E0D\u51C6\u786E\u7684\uFF0C\u4E0D\u6E05\u6670\u7684\uFF0C\u6A21\u7CCA\u7684)\u3002 \u771F\u5B9E\u5EA6\u7ECF\u5E38\u6DF7\u6DC6\u4E8E\u6982\u7387\u3002\u4F46\u662F\u5B83\u4EEC\u5728\u6982\u5FF5\u4E0A\u662F\u4E0D\u4E00\u6837\u7684\uFF1B\u6A21\u7CCA\u771F\u503C\u8868\u793A\u5728\u6A21\u7CCA\u5B9A\u4E49\u7684\u96C6\u5408\u4E2D\u7684\u6210\u5458\u6B78\u5C6C\u5173\u7CFB\uFF0C\u800C\u4E0D\u662F\u67D0\u4E8B\u4EF6\u6216\u6761\u4EF6\u7684\u53EF\u80FD\u5EA6(likelihood)\u3002\u8981\u5C55\u793A\u8FD9\u79CD\u533A\u522B\uFF0C\u8003\u8651\u4E0B\u5217\u60C5\u8282: Bob \u5728\u6709\u4E24\u4E2A\u6BD7\u90BB\u7684\u5C4B\u5B50\u7684\u623F\u5B50\u4E2D: \u53A8\u623F\u548C\u9910\u5385\u3002\u5728\u5F88\u591A\u60C5\u51B5\u4E0B\uFF0CBob \u7684\u72B6\u6001\u662F\u5728\u4E8B\u7269\u201C\u5728\u53A8\u623F\u4E2D\u201D\u7684\u96C6\u5408\u5185\u662F\u5B8C\u5168\u660E\u786E\u7684: \u4ED6\u8981\u4E48\u201C\u5728\u53A8\u623F\u4E2D\u201D \u8981\u4E48\u201C\u4E0D\u5728\u53A8\u623F\u4E2D\u201D\u3002\u4F46 Bob \u7AD9\u5728\u95E8\u53E3\u7684\u65F6\u5019\u600E\u4E48\u529E\u5462? \u5B83\u53EF\u88AB\u8BA4\u4E3A\u662F\u201C\u90E8\u5206\u7684\u5728\u53A8\u623F\u4E2D\u201D\u3002\u91CF\u5316\u8FD9\u4E2A\u90E8\u5206\u9648\u8FF0\u4EA7\u751F\u4E86\u4E00\u4E2A\u6A21\u7CCA\u96C6\u5408\u6210\u5458\u5173\u7CFB\u3002\u6BD4\u5982\uFF0C\u53EA\u6709\u4ED6\u7684\u5C0F\u811A\u8DBE\u5728\u9910\u5385\uFF0C\u6211\u4EEC\u53EF\u4EE5\u8BF4 Bob \u662F 0.99\u201C\u5728\u53A8\u623F\u4E2D\u201D\u3002\u53EA\u8981 Bob \u7AD9\u5728\u4E86\u95E8\u53E3\uFF0C\u5C31\u6CA1\u6709\u4E8B\u4EF6(\u5982\u629B\u786C\u5E01)\u80FD\u89E3\u51B3\u4ED6\u5B8C\u5168\u7684\u201C\u5728\u53A8\u623F\u4E2D\u201D\u6216\u201C\u4E0D\u5728\u53A8\u623F\u4E2D\u201D\u3002\u6A21\u7CCA\u96C6\u5408\u662F\u57FA\u4E8E\u96C6\u5408\u7684\u6A21\u7CCA\u5B9A\u4E49\u800C\u4E0D\u662F\u968F\u673A\u6027\u3002 \u6A21\u7CCA\u903B\u8F91\u5141\u8BB8\u5728\u5305\u542B 0 \u548C 1 \u7684\u5B83\u4EEC\u4E4B\u95F4\u96C6\u5408\u6210\u5458\u5173\u7CFB\u503C\uFF0C\u540C\u4E8E\u9ED1\u548C\u767D\u4E4B\u95F4\u7684\u7070\u8272\uFF0C\u5728\u5B83\u7684\u8BED\u8A00\u5F62\u5F0F\u4E2D\uFF0C\u6709\u4E0D\u7CBE\u786E\u7684\u6982\u5FF5\u5982\"\u7A0D\u5FAE\"\u3001\"\u76F8\u5F53\"\u548C\"\u975E\u5E38\"\u3002\u7279\u522B\u662F\uFF0C\u5B83\u5141\u8BB8\u5728\u96C6\u5408\u4E2D\u7684\u90E8\u5206\u6210\u5458\u5173\u7CFB\u3002\u5B83\u6709\u5173\u4E8E\u6A21\u7CCA\u96C6\u5408\u548C\u53EF\u80FD\u6027\u7406\u8BBA\u3002\u5B83\u662F1965\u5E74\u5362\u83F2\u7279\u00B7\u6CFD\u5FB7\u6559\u6388\u5728\u52A0\u6D32\u5927\u5B66\u4F2F\u514B\u529B\u5206\u6821\u4ECB\u5165\u7684\u3002 \u6A21\u7CCA\u903B\u8F91\u5C3D\u7BA1\u88AB\u5E7F\u6CDB\u63A5\u53D7\u5374\u662F\u6709\u4E89\u8BAE\u7684: \u5B83\u88AB\u67D0\u4E9B\u63A7\u5236\u5DE5\u7A0B\u5E08\u51FA\u4E8E\u6709\u6548\u6027\u548C\u5176\u4ED6\u539F\u56E0\uFF0C\u548C\u4E00\u4E9B\u575A\u6301\u6982\u7387\u8BBA\u662F\u4E0D\u786E\u5B9A\u6027\u7684\u552F\u4E00\u4E25\u683C\u63CF\u8FF0\u7684\u7EDF\u8BA1\u5B66\u5BB6\u6240\u62D2\u7EDD\u3002\u6279\u8BC4\u8005\u8FD8\u6279\u8BC4\u5B83\u4E0D\u80FD\u662F\u666E\u901A\u96C6\u5408\u8BBA\u7684\u8D85\u96C6\uFF0C\u56E0\u4E3A\u6210\u5458\u51FD\u6570\u662F\u4F9D\u636E\u5E38\u89C4\u96C6\u5408\u800C\u5B9A\u4E49\u7684\u3002"@zh ,
"Logica fuzzy a fost definit\u0103 \u00EEn 1965 de c\u0103tre prof. Lotfi Zadeh, de la Universitatea Berkeley. Spre deosebire de logica clasic\u0103, care lucreaz\u0103 cu dou\u0103 valori numerice exacte (0 pentru fals \u015Fi 1 pentru adev\u0103rat), logica fuzzy folose\u015Fte o plaj\u0103 continu\u0103 de valori logice cuprinse \u00EEn intervalul 0-1, unde 0 indic\u0103 falsitatea complet\u0103, iar 1 indic\u0103 adev\u0103rul complet. Astfel, dac\u0103 \u00EEn logica clasic\u0103 un obiect poate apar\u0163ine (1) sau nu (0) unei mul\u0163imi date, \u00EEn logica fuzzy putem defini gradul de apartenen\u0163\u0103 al obiectului la mul\u0163ime \u015Fi care poate lua valori \u00EEntre 0 \u015Fi 1. Logica fuzzy ofer\u0103 instrumentele necesare pentru reprezentarea \u00EEn sistemele inteligente a unor concepte imprecise cum sunt \u201Emare\u201D, \u201Emic\u201D, \u201Escump\u201D, \u201Eieftin\u201D \u015F.a. , concepte numite variabile lingvistice sau variabile fuzzy. Pentru reprezentarea acestora se folosesc seturile fuzzy, care capteaz\u0103 din punct de vedere cantitativ interpretarea calitativ\u0103 a termenilor."@ro ,
"\u041D\u0435\u0447\u0451\u0442\u043A\u0430\u044F \u043B\u043E\u0433\u0438\u043A\u0430 \u0438 \u0442\u0435\u043E\u0440\u0438\u044F \u043D\u0435\u0447\u0451\u0442\u043A\u0438\u0445 \u043C\u043D\u043E\u0436\u0435\u0441\u0442\u0432 - \u0440\u0430\u0437\u0434\u0435\u043B \u043C\u0430\u0442\u0435\u043C\u0430\u0442\u0438\u043A\u0438, \u044F\u0432\u043B\u044F\u044E\u0449\u0438\u0439\u0441\u044F \u043E\u0431\u043E\u0431\u0449\u0435\u043D\u0438\u0435\u043C \u043A\u043B\u0430\u0441\u0441\u0438\u0447\u0435\u0441\u043A\u043E\u0439 \u043B\u043E\u0433\u0438\u043A\u0438 \u0438 \u0442\u0435\u043E\u0440\u0438\u0438 \u043C\u043D\u043E\u0436\u0435\u0441\u0442\u0432. \u041F\u043E\u043D\u044F\u0442\u0438\u0435 \u043D\u0435\u0447\u0435\u0442\u043A\u043E\u0439 \u043B\u043E\u0433\u0438\u043A\u0438 \u0431\u044B\u043B\u043E \u0432\u043F\u0435\u0440\u0432\u044B\u0435 \u0432\u0432\u0435\u0434\u0435\u043D\u043E \u043F\u0440\u043E\u0444\u0435\u0441\u0441\u043E\u0440\u043E\u043C \u041B\u044E\u0442\u0444\u0438 \u0417\u0430\u0434\u0435 \u0432 1965 \u0433."@ru ,
"Sumea logiikka on matemaattisen logiikan laajennus, jossa propositiolla on diskreetin totuusarvon (tosi tai ep\u00E4tosi) sijasta reaalinen totuusarvo suljetulla v\u00E4lill\u00E4 nollasta yhteen."@fi ,
"\u30D5\u30A1\u30B8\u30A3\u8AD6\u7406\uFF08-\u308D\u3093\u308A\u3001\u82F1: Fuzzy logic\uFF09\u306F\u3001\u30D5\u30A1\u30B8\u30A3\u96C6\u5408\u8AD6\u304B\u3089\u6D3E\u751F\u3057\u305F\u3082\u306E\u3067\u3001\u53E4\u5178\u7684\u306A\u4E00\u968E\u8FF0\u8A9E\u8AD6\u7406\u306E\u53B3\u5BC6\u306A\u63A8\u8AD6\u3068\u306F\u7570\u306A\u308B\u8FD1\u4F3C\u7684\u306A\u63A8\u8AD6\u3092\u6271\u3046\u8AD6\u7406\u4F53\u7CFB\u3067\u3042\u308B\u3002\u30D5\u30A1\u30B8\u30A3\u96C6\u5408\u8AD6\u306E\u5FDC\u7528\u9762\u3068\u3055\u308C\u3001\u5B9F\u4E16\u754C\u306E\u8907\u96D1\u306A\u554F\u984C\u3092\u6271\u3046\uFF08Klir 1997\uFF09\u3002\u30D5\u30A1\u30B8\u30A3\u8AD6\u7406\u306F1965\u5E74\u3001\u30AB\u30EA\u30D5\u30A9\u30EB\u30CB\u30A2\u5927\u5B66\u30D0\u30FC\u30AF\u30EC\u30FC\u6821\u306E\u30ED\u30C8\u30D5\u30A3\u30FB\u30B6\u30C7\u30FC\u304C\u751F\u307F\u51FA\u3057\u305F\u3002"@ja ,
"Fuzzy logika je podobor matematiky odvozen\u00FD od teorie fuzzy mno\u017Ein, ve kter\u00E9m se logick\u00E9 v\u00FDroky ohodnocuj\u00ED stupn\u011Bm p\u0159\u00EDslu\u0161nosti (tak\u00E9 index v\u00E1gnosti), jeho\u017E hodnoty jsou v intervalu od 0 do 1. Li\u0161\u00ED se tak od klasick\u00E9 v\u00FDrokov\u00E9 a predik\u00E1tov\u00E9 logiky, v nich\u017E se v\u00FDroky ohodnocuj\u00ED bu\u010F jako pravdiv\u00E9, nebo nepravdiv\u00E9 \u2014 v bin\u00E1rn\u00EDm vyj\u00E1d\u0159en\u00ED jako 1, nebo 0. Fuzzy logika je mnohem vhodn\u011Bj\u0161\u00ED pro \u0159adu re\u00E1ln\u00FDch rozhodovac\u00EDch \u00FAloh. Pou\u017E\u00EDv\u00E1 se nap\u0159\u00EDklad v my\u010Dk\u00E1ch na n\u00E1dob\u00ED, pra\u010Dk\u00E1ch, autopilotech, parkovac\u00EDch senzorech atd. Fuzzy logika byla zavedena roku 1965 Lotfim Zadehem z Kalifornsk\u00E9 univerzity v Berkeley. Funkce p\u0159\u00EDslu\u0161nosti ve fuzzy logice umo\u017E\u0148uje p\u0159i\u0159adit p\u0159\u00EDslu\u0161nost k mno\u017Ein\u00E1m v rozmez\u00ED od 0 do 1, v\u010Detn\u011B obou hrani\u010Dn\u00EDch hodnot. Fuzzy logika tak umo\u017E\u0148uje matematicky vyj\u00E1d\u0159it pojmy jako \u201Etrochu\u201C, \u201Edost\u201C nebo \u201Ehodn\u011B\u201C. P\u0159esn\u011Bji, umo\u017E\u0148uje vyj\u00E1d\u0159it \u010D\u00E1ste\u010Dnou p\u0159\u00EDslu\u0161nost k mno\u017Ein\u011B. Stupe\u0148 p\u0159\u00EDslu\u0161nosti je \u010Dasto zam\u011B\u0148ov\u00E1n s pravd\u011Bpodobnost\u00ED. Tyto pojmy jsou ale rozd\u00EDln\u00E9. Fuzzy hodnota je p\u0159i\u0159azena funkc\u00ED p\u0159\u00EDslu\u0161nosti k v\u00E1gn\u011B definovan\u00FDm mno\u017Ein\u00E1m a nep\u0159edstavuje pravd\u011Bpodobnost n\u011Bjak\u00E9ho jevu."@cs ,
"Fuzzy logic is a form of multi-valued logic derived from fuzzy set theory to deal with reasoning that is approximate rather than precise. In contrast with binary sets having binary logic, also known as crisp logic, the fuzzy logic variables may have a membership value of not only 0 or 1. Just as in fuzzy set theory with fuzzy logic the set membership values can range (inclusively) between 0 and 1, in fuzzy logic the degree of truth of a statement can range between 0 and 1 and is not constrained to the two truth values {true (1), false (0)} as in classic propositional logic. And when linguistic variables are used, these degrees may be managed by specific functions, as discussed below. The term \"fuzzy logic\" emerged as a consequence of the development of the theory of fuzzy sets by Lotfi Zadeh. In 1965, Lotfi Zadeh proposed fuzzy set theory, and later established fuzzy logic based on fuzzy sets. Fuzzy logic has been applied to diverse fields, from control theory to artificial intelligence, yet still remains controversial among most statisticians, who prefer Bayesian logic, and some control engineers, who prefer traditional two-valued logic. Earlier than Zadeh, a paper introducing the concept without using the term \"fuzzy\" was published by R.H. Wilkinson in 1963 and thus preceded fuzzy set theory. Wilkinson was the first one to redefine and generalize the earlier multivalued logics in terms of set theory. The main purpose of his paper, following his first proposals in his 1961 electrical engineering master thesis, was to show how any mathematical function could be simulated using hardwired analog electronic circuits. He did this by first creating various linear voltage ramps which were then selected in a logic block using diodes and resistor circuits which implemented the maximum and minimum fuzzy logic rules of the INCLUSIVE OR and the AND operations respectively. He called his logic \"analog logic\". Some say that the idea of fuzzy logic is set-theoretical equivalent of the \"analog logic\" of Wilkinson (without recourse to electrical circuits), but he never received any credit."@en ,
"Logika rozmyta, jedna z logik wielowarto\u015Bciowych (ang. multi-valued logic), stanowi uog\u00F3lnienie klasycznej dwuwarto\u015Bciowej logiki. Jest \u015Bci\u015Ble powi\u0105zana z teori\u0105 zbior\u00F3w rozmytych i teori\u0105 prawdopodobie\u0144stwa. Zosta\u0142a zaproponowana przez Lotfi Zadeha w 1965 roku. W logice rozmytej mi\u0119dzy stanem 0 a stanem 1 rozci\u0105ga si\u0119 szereg warto\u015Bci po\u015Brednich, kt\u00F3re okre\u015Blaj\u0105 stopie\u0144 przynale\u017Cno\u015Bci elementu do zbioru. Logika rozmyta okaza\u0142a si\u0119 bardzo przydatna w zastosowaniach in\u017Cynierskich, czyli tam, gdzie klasyczna logika klasyfikuj\u0105ca jedynie wed\u0142ug kryterium prawda/fa\u0142sz nie potrafi skutecznie poradzi\u0107 sobie z wieloma niejednoznaczno\u015Bciami i sprzeczno\u015Bciami. Znajduje wiele zastosowa\u0144, mi\u0119dzy innymi w elektronicznych systemach sterowania, zadaniach eksploracji danych czy te\u017C w budowie system\u00F3w ekspertowych. Metody logiki rozmytej wraz z algorytmami ewolucyjnymi i sieciami neuronowymi stanowi\u0105 nowoczesne narz\u0119dzia do budowy inteligentnych system\u00F3w maj\u0105cych zdolno\u015Bci uog\u00F3lniania wiedzy."@pl ,
"La logica fuzzy o logica sfumata o logica sfocata \u00E8 una logica in cui si pu\u00F2 attribuire a ciascuna proposizione un grado di verit\u00E0 compreso tra 0 e 1. \u00C8 una logica polivalente, e pertanto un'estensione della logica booleana. \u00C8 fortemente legata alla teoria degli insiemi sfocati e, gi\u00E0 intuita da Cartesio, Bertrand Russell, Albert Einstein, Werner Karl Heisenberg, Jan \u0141ukasiewicz e Max Black, venne concretizzata da Lotfi Zadeh. Con grado di verit\u00E0 o valore di appartenenza si intende quanto \u00E8 vera una propriet\u00E0: questa pu\u00F2 essere, oltre che vera (= a valore 1) o falsa (= a valore 0) come nella logica classica, anche pari a valori intermedi. Si pu\u00F2 ad esempio dire che: un neonato nato \u00E8 \"giovane\" di valore 1, un diciottenne \u00E8 \"giovane\" di valore 0,8, ed un sessantacinquenne \u00E8 \"giovane\" di valore 0,15. Solitamente il valore di appartenenza si indica con \u03BC; il valore di appartenenza ad un insieme fuzzy F di un predicato p si indica con \u00B5F(p)."@it ,
"Suddig logik (engelska fuzzy logic), vanligen kallad Oskarp logik, utvecklad av Lotfi Asker Zadeh under 1960- och 70-talen, \u00E4r en form av logik d\u00E4r lagen om det uteslutna tredje inte g\u00E4ller. I fuzzy logic kan en proposition vara delvis sann och delvis falsk, vilket resulterar i en gradskala av sanning. Man anv\u00E4nder oftast reella tal fr\u00E5n 0 till 1 som sanningsv\u00E4rden, d\u00E4r 0 st\u00E5r f\u00F6r tvekl\u00F6st falskt och 1 f\u00F6r tvekl\u00F6st sant och v\u00E4rden d\u00E4remellan st\u00E5r f\u00F6r gradskillnader mellan falskt och sant. Ett exempel \u00E4r p\u00E5st\u00E5endet Anna \u00E4r l\u00E5ng, vars sanningshalt kan debatteras om Annas l\u00E4ngd inte \u00E4r mycket avvikande fr\u00E5n det normala. Logiken kallas suddig eftersom man utg\u00E5r fr\u00E5n att p\u00E5st\u00E5endens sanningshalt kan vara oklara."@sv ,
"Fuzzylogik (engl. fuzzy \u201Averschwommen\u2018, fuzzy logic, fuzzy theory \u201Averschwommene Logik\u2018 bzw. \u201Averschwommene Theorie\u2018) ist eine Theorie, welche vor allem f\u00FCr die Modellierung von Unsicherheiten und Unsch\u00E4rfen von umgangssprachlichen Beschreibungen entwickelt wurde. Sie ist eine Verallgemeinerung der zweiwertigen Booleschen Logik. Beispielsweise kann damit die sogenannte \"Fuzziness\" von Angaben wie \"ein bisschen\", \"ziemlich\" oder \"stark\" mathematisch in Modellen erfasst werden. Die Fuzzylogik basiert auf den Fuzzy-Mengen (Fuzzy-Sets) und sogenannten Zugeh\u00F6rigkeitsfunktionen, die Objekte auf Fuzzy-Mengen abbilden, sowie passenden logischen Operationen auf diesen Mengen und ihrer Inferenz. Bei technischen Anwendungen m\u00FCssen au\u00DFerdem Methoden zur Fuzzyfizierung und Defuzzyfizierung betrachtet werden, das hei\u00DFt Methoden zur Umwandlung von Angaben und Zusammenh\u00E4ngen in Fuzzylogik und wieder zur\u00FCck, zum Beispiel als Stellwert f\u00FCr eine Heizung als Resultat."@de ,
"A l\u00F3gica difusa ou l\u00F3gica fuzzy \u00E9 uma extens\u00E3o da l\u00F3gica booleana que admite valores l\u00F3gicos intermedi\u00E1rios entre o FALSO(0) e o VERDADEIRO(1); por exemplo o valor m\u00E9dio 'TALVEZ' (0,5). Isto significa que um valor l\u00F3gico difuso \u00E9 um valor qualquer no intervalo de valores entre 0 e 1. Este tipo de l\u00F3gica engloba de certa forma conceitos estat\u00EDsticos principalmente na \u00E1rea de Infer\u00EAncia. As implementa\u00E7\u00F5es da l\u00F3gica difusa permitem que estados indeterminados possam ser tratados por dispositivos de controle. Desse modo, \u00E9 poss\u00EDvel avaliar conceitos n\u00E3o-quantific\u00E1veis. Casos pr\u00E1ticos: avaliar a temperatura (quente,morno, m\u00E9dio,etc.. ), o sentimento de felicidade(radiante,feliz,ap\u00E1tico,triste.. ), a veracidade de um argumento (correct\u00EDssimo,correcto,contra-argumentativo,incoerente,falso,totalmente err\u00F3neo, etc.. ) A l\u00F3gica fuzzy deve ser vista mais como uma \u00E1rea de pesquisa sobre tratamento da incerteza, ou uma fam\u00EDlia de modelos matem\u00E1ticos dedicados ao tratamento da incerteza, do que uma l\u00F3gica propriamente dita. A l\u00F3gica difusa normalmente est\u00E1 associada ao uso da teoria de conjuntos fuzzy proposto por \u0141ukasiewicz. Ao trabalhar com a l\u00F3gica fuzzy \u00E9 comum chamar a l\u00F3gica booleana de l\u00F3gica n\u00EDtida. Muitos pesquisadores de vers\u00F5es booleanas de l\u00F3gica n\u00E3o aceitam a l\u00F3gica fuzzy como uma verdadeira l\u00F3gica, no sentido em que aceitam, por exemplo, a l\u00F3gica modal. Isso pode ser associado a diferentes fatos, entre eles o fato de muitos modelos permitirem solu\u00E7\u00F5es aproximadas que n\u00E3o correspondem a uma \"verdade\" l\u00F3gica."@pt ,
"La logique floue (fuzzy logic, en anglais) est une technique utilis\u00E9e en intelligence artificielle. Elle a \u00E9t\u00E9 formalis\u00E9e par Lotfi Zadeh en 1965 et utilis\u00E9e dans des domaines aussi vari\u00E9s que l'automatisme, la robotique (reconnaissance de formes), la gestion de la circulation routi\u00E8re (feux rouges), le contr\u00F4le a\u00E9rien, l'environnement, la m\u00E9decine, l'assurance (s\u00E9lection et pr\u00E9vention des risques) et bien d'autres. En fait, le simple fait de noter, d\u00E9j\u00E0 sous Jules Ferry, un \u00E9l\u00E8ve dans diff\u00E9rentes disciplines et de lui calculer un rang par application de coefficients \u00E0 ses notes constituait d\u00E9j\u00E0 une certaine forme de logique floue. Elle s'appuie sur la th\u00E9orie math\u00E9matique des ensembles flous. Cette th\u00E9orie, introduite par Zadeh, est une extension de la th\u00E9orie des ensembles classiques pour la prise en compte d'ensembles d\u00E9finis de fa\u00E7on impr\u00E9cise. C'est une th\u00E9orie formelle et math\u00E9matique dans le sens o\u00F9 Zadeh, en partant du concept de fonction d'appartenance pour mod\u00E9liser la d\u00E9finition d'un sous-ensemble d'un univers donn\u00E9, a \u00E9labor\u00E9 un mod\u00E8le complet de propri\u00E9t\u00E9s et de d\u00E9finitions formelles. Il a aussi montr\u00E9 que cette th\u00E9orie des sous-ensembles flous se r\u00E9duit effectivement \u00E0 la th\u00E9orie des sous-ensembles classiques dans le cas o\u00F9 les fonctions d'appartenance consid\u00E9r\u00E9es prennent des valeurs binaires ({0,1}). Elle pr\u00E9sente aussi l'int\u00E9r\u00EAt d'\u00EAtre plus facile et meilleur march\u00E9 \u00E0 impl\u00E9menter qu'une logique probabiliste, bien que cette derni\u00E8re seule soit stricto sensu coh\u00E9rente. Par exemple la courbe Ev(p) peut \u00EAtre remplac\u00E9e par trois segments de droite sans perte excessive de pr\u00E9cision pour beaucoup d'applications consid\u00E9r\u00E9es ci-dessus."@fr ;
rdfs:comment "Fuzzy logika je podobor matematiky odvozen\u00FD od teorie fuzzy mno\u017Ein, ve kter\u00E9m se logick\u00E9 v\u00FDroky ohodnocuj\u00ED stupn\u011Bm p\u0159\u00EDslu\u0161nosti (tak\u00E9 index v\u00E1gnosti), jeho\u017E hodnoty jsou v intervalu od 0 do 1. Li\u0161\u00ED se tak od klasick\u00E9 v\u00FDrokov\u00E9 a predik\u00E1tov\u00E9 logiky, v nich\u017E se v\u00FDroky ohodnocuj\u00ED bu\u010F jako pravdiv\u00E9, nebo nepravdiv\u00E9 \u2014 v bin\u00E1rn\u00EDm vyj\u00E1d\u0159en\u00ED jako 1, nebo 0. Fuzzy logika je mnohem vhodn\u011Bj\u0161\u00ED pro \u0159adu re\u00E1ln\u00FDch rozhodovac\u00EDch \u00FAloh."@cs ,
"Fuzzy logic is a form of multi-valued logic derived from fuzzy set theory to deal with reasoning that is approximate rather than precise. In contrast with binary sets having binary logic, also known as crisp logic, the fuzzy logic variables may have a membership value of not only 0 or 1."@en ,
"La logica fuzzy o logica sfumata o logica sfocata \u00E8 una logica in cui si pu\u00F2 attribuire a ciascuna proposizione un grado di verit\u00E0 compreso tra 0 e 1. \u00C8 una logica polivalente, e pertanto un'estensione della logica booleana. \u00C8 fortemente legata alla teoria degli insiemi sfocati e, gi\u00E0 intuita da Cartesio, Bertrand Russell, Albert Einstein, Werner Karl Heisenberg, Jan \u0141ukasiewicz e Max Black, venne concretizzata da Lotfi Zadeh."@it ,
"\u041D\u0435\u0447\u0456\u0442\u043A\u0430 \u043B\u043E\u0433\u0456\u043A\u0430 (\u0432\u0456\u0434 \u0430\u043D\u0433\u043B. fuzzy logic) \u044F\u043A \u043D\u0430\u0443\u043A\u0430 \u0431\u0443\u043B\u0430 \u0437\u0430\u043F\u043E\u0447\u0430\u0442\u043A\u043E\u0432\u0430\u043D\u0430 \u0430\u043C\u0435\u0440\u0438\u043A\u0430\u043D\u0441\u044C\u043A\u0438\u043C \u0432\u0447\u0435\u043D\u0438\u043C \u0456\u0440\u0430\u043D\u0441\u044C\u043A\u043E\u0433\u043E \u043F\u043E\u0445\u043E\u0434\u0436\u0435\u043D\u043D\u044F \u041B\u043E\u0442\u0444\u0456 \u0410. \u0417\u0430\u0434\u0435 (Lotfi A. Zadeh)."@uk ,
"Suddig logik (engelska fuzzy logic), vanligen kallad Oskarp logik, utvecklad av Lotfi Asker Zadeh under 1960- och 70-talen, \u00E4r en form av logik d\u00E4r lagen om det uteslutna tredje inte g\u00E4ller. I fuzzy logic kan en proposition vara delvis sann och delvis falsk, vilket resulterar i en gradskala av sanning."@sv ,
"\u041D\u0435\u0447\u0451\u0442\u043A\u0430\u044F \u043B\u043E\u0433\u0438\u043A\u0430 \u0438 \u0442\u0435\u043E\u0440\u0438\u044F \u043D\u0435\u0447\u0451\u0442\u043A\u0438\u0445 \u043C\u043D\u043E\u0436\u0435\u0441\u0442\u0432 - \u0440\u0430\u0437\u0434\u0435\u043B \u043C\u0430\u0442\u0435\u043C\u0430\u0442\u0438\u043A\u0438, \u044F\u0432\u043B\u044F\u044E\u0449\u0438\u0439\u0441\u044F \u043E\u0431\u043E\u0431\u0449\u0435\u043D\u0438\u0435\u043C \u043A\u043B\u0430\u0441\u0441\u0438\u0447\u0435\u0441\u043A\u043E\u0439 \u043B\u043E\u0433\u0438\u043A\u0438 \u0438 \u0442\u0435\u043E\u0440\u0438\u0438 \u043C\u043D\u043E\u0436\u0435\u0441\u0442\u0432. \u041F\u043E\u043D\u044F\u0442\u0438\u0435 \u043D\u0435\u0447\u0435\u0442\u043A\u043E\u0439 \u043B\u043E\u0433\u0438\u043A\u0438 \u0431\u044B\u043B\u043E \u0432\u043F\u0435\u0440\u0432\u044B\u0435 \u0432\u0432\u0435\u0434\u0435\u043D\u043E \u043F\u0440\u043E\u0444\u0435\u0441\u0441\u043E\u0440\u043E\u043C \u041B\u044E\u0442\u0444\u0438 \u0417\u0430\u0434\u0435 \u0432 1965 \u0433."@ru ,
""@zh ,
"La l\u00F3gica borrosa o difusa se basa en lo relativo de lo observado. Este tipo de l\u00F3gica toma dos valores aleatorios, pero contextualizados y referidos entre s\u00ED. As\u00ED, por ejemplo, una persona que mida 2 metros es claramente una persona alta, si previamente se ha tomado el valor de persona baja y se ha establecido en 1 metro. Ambos valores est\u00E1n contextualizados a personas y referidos a una medida m\u00E9trica lineal."@es ,
"Logica fuzzy a fost definit\u0103 \u00EEn 1965 de c\u0103tre prof. Lotfi Zadeh, de la Universitatea Berkeley. Spre deosebire de logica clasic\u0103, care lucreaz\u0103 cu dou\u0103 valori numerice exacte (0 pentru fals \u015Fi 1 pentru adev\u0103rat), logica fuzzy folose\u015Fte o plaj\u0103 continu\u0103 de valori logice cuprinse \u00EEn intervalul 0-1, unde 0 indic\u0103 falsitatea complet\u0103, iar 1 indic\u0103 adev\u0103rul complet."@ro ,
"Az elmos\u00F3dott halmazok logik\u00E1ja a t\u00F6bb\u00E9rt\u00E9k\u0171 logikai szemantik\u00E1k egyike. Tulajdonk\u00E9ppen fuzzy logika n\u00E9v alatt egy eg\u00E9sz elm\u00E9letcsal\u00E1dr\u00F3l besz\u00E9lhet\u00FCnk, melynek sokr\u00E9t\u0171 alkalmaz\u00E1sai vannak els\u0151sorban az informatik\u00E1ban, de alkalmaz\u00E1sra tal\u00E1lt a nyelvtudom\u00E1nyi \u00E9s logikai szemantik\u00E1ban, a matematikai logik\u00E1ban \u00E9s a val\u00F3sz\u00EDn\u0171s\u00E9gelm\u00E9letben is."@hu ,
"Fuzzylogikk inneb\u00E6rer at man baserer avgj\u00F8relser p\u00E5 'godt nok' eller 'n\u00E6r nok' vurderinger. Det finnes mange former for Fuzzylogikk og mange ulike algoritmer for \u00E5 beregne n\u00E6rhet. Et eksempel er innen navnematching. En person som heter Petter Hansen kan feilaktig registreres som Peter Hansen eller Petter Hanssen. Dermed kan dubletter i registere oppst\u00E5, og man f\u00E5r behov for \u00E5 sl\u00E5 sammen variasjoner av ett individ."@no ,
"Logika rozmyta, jedna z logik wielowarto\u015Bciowych (ang. multi-valued logic), stanowi uog\u00F3lnienie klasycznej dwuwarto\u015Bciowej logiki. Jest \u015Bci\u015Ble powi\u0105zana z teori\u0105 zbior\u00F3w rozmytych i teori\u0105 prawdopodobie\u0144stwa. Zosta\u0142a zaproponowana przez Lotfi Zadeha w 1965 roku. W logice rozmytej mi\u0119dzy stanem 0 a stanem 1 rozci\u0105ga si\u0119 szereg warto\u015Bci po\u015Brednich, kt\u00F3re okre\u015Blaj\u0105 stopie\u0144 przynale\u017Cno\u015Bci elementu do zbioru."@pl ,
"Sumea logiikka on matemaattisen logiikan laajennus, jossa propositiolla on diskreetin totuusarvon (tosi tai ep\u00E4tosi) sijasta reaalinen totuusarvo suljetulla v\u00E4lill\u00E4 nollasta yhteen."@fi ,
"\u30D5\u30A1\u30B8\u30A3\u8AD6\u7406\uFF08-\u308D\u3093\u308A\u3001\u82F1: Fuzzy logic\uFF09\u306F\u3001\u30D5\u30A1\u30B8\u30A3\u96C6\u5408\u8AD6\u304B\u3089\u6D3E\u751F\u3057\u305F\u3082\u306E\u3067\u3001\u53E4\u5178\u7684\u306A\u4E00\u968E\u8FF0\u8A9E\u8AD6\u7406\u306E\u53B3\u5BC6\u306A\u63A8\u8AD6\u3068\u306F\u7570\u306A\u308B\u8FD1\u4F3C\u7684\u306A\u63A8\u8AD6\u3092\u6271\u3046\u8AD6\u7406\u4F53\u7CFB\u3067\u3042\u308B\u3002\u30D5\u30A1\u30B8\u30A3\u96C6\u5408\u8AD6\u306E\u5FDC\u7528\u9762\u3068\u3055\u308C\u3001\u5B9F\u4E16\u754C\u306E\u8907\u96D1\u306A\u554F\u984C\u3092\u6271\u3046\uFF08Klir 1997\uFF09\u3002\u30D5\u30A1\u30B8\u30A3\u8AD6\u7406\u306F1965\u5E74\u3001\u30AB\u30EA\u30D5\u30A9\u30EB\u30CB\u30A2\u5927\u5B66\u30D0\u30FC\u30AF\u30EC\u30FC\u6821\u306E\u30ED\u30C8\u30D5\u30A3\u30FB\u30B6\u30C7\u30FC\u304C\u751F\u307F\u51FA\u3057\u305F\u3002"@ja ,
"Fuzzy logic (soms vage logica of wollige logica genoemd) is een stroming binnen de logica. Zij kan gezien worden als een uitbreiding van Booleaanse (boolean) logica. Het principe uit de Booleaanse logica dat iets of waar of onwaar is, wordt losgelaten; het is dus een vorm van meerwaardige logica. In plaats daarvan worden er waarheidswaarden gebruikt tussen 0 (onwaar) en 1 (waar) in."@nl ,
"Fuzzylogik (engl. fuzzy \u201Averschwommen\u2018, fuzzy logic, fuzzy theory \u201Averschwommene Logik\u2018 bzw. \u201Averschwommene Theorie\u2018) ist eine Theorie, welche vor allem f\u00FCr die Modellierung von Unsicherheiten und Unsch\u00E4rfen von umgangssprachlichen Beschreibungen entwickelt wurde. Sie ist eine Verallgemeinerung der zweiwertigen Booleschen Logik. Beispielsweise kann damit die sogenannte \"Fuzziness\" von Angaben wie \"ein bisschen\", \"ziemlich\" oder \"stark\" mathematisch in Modellen erfasst werden."@de ,
"Bulan\u0131k mant\u0131k, 1961 y\u0131l\u0131nda L\u00FCtfi Askerzade'nin yay\u0131nlad\u0131\u011F\u0131 bir makalenin sonucu olu\u015Fmu\u015F bir mant\u0131k yap\u0131s\u0131d\u0131r. Bulan\u0131k mant\u0131\u011F\u0131n temeli bulan\u0131k k\u00FCme ve alt k\u00FCmelere dayan\u0131r. Klasik yakla\u015F\u0131mda bir varl\u0131k ya k\u00FCmenin eleman\u0131d\u0131r ya da de\u011Fildir. Matematiksel olarak ifade edildi\u011Finde varl\u0131k k\u00FCme ile olan \u00FCyelik ili\u015Fkisi bak\u0131m\u0131ndan k\u00FCmenin eleman\u0131 oldu\u011Funda \"1\", k\u00FCmenin eleman\u0131 olmad\u0131\u011F\u0131 zaman \"0\" de\u011Ferini al\u0131r."@tr ,
"A l\u00F3gica difusa ou l\u00F3gica fuzzy \u00E9 uma extens\u00E3o da l\u00F3gica booleana que admite valores l\u00F3gicos intermedi\u00E1rios entre o FALSO(0) e o VERDADEIRO(1); por exemplo o valor m\u00E9dio 'TALVEZ' (0,5). Isto significa que um valor l\u00F3gico difuso \u00E9 um valor qualquer no intervalo de valores entre 0 e 1. Este tipo de l\u00F3gica engloba de certa forma conceitos estat\u00EDsticos principalmente na \u00E1rea de Infer\u00EAncia."@pt ,
"La logique floue (fuzzy logic, en anglais) est une technique utilis\u00E9e en intelligence artificielle. Elle a \u00E9t\u00E9 formalis\u00E9e par Lotfi Zadeh en 1965 et utilis\u00E9e dans des domaines aussi vari\u00E9s que l'automatisme, la robotique (reconnaissance de formes), la gestion de la circulation routi\u00E8re (feux rouges), le contr\u00F4le a\u00E9rien, l'environnement, la m\u00E9decine, l'assurance (s\u00E9lection et pr\u00E9vention des risques) et bien d'autres."@fr .
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