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附件：英文版学术论文格式样张The Researches on Rs Method for Discrete Membership Functions（空一行）ZHANG Xiaoya, LI Dexiang(题目14号字加黑居中) School of Management,Sichuan University, P.R.China, 610036 (10 号字居中)yuanfengxiangsheng@(10号字加黑) (空一行)Abstract Mizumoto used to advance a fuzzy reasoning method ,Rs,which fits the……Key words IDSS,Fuzzy reasoning,……号字)(空一行)1 Introduction (一级标题12号字加黑)We know that the approaches of implementation of intelligent decision support systems(IDSS)have become variable……(正文均用10号字)(空一行)2 An ExampleAccording to the definition of Rs,we can construct the fuzzy relation matrix,as shown in table 1Table 1 A Fuzzy Relation Rs (9号字加黑居中) U2U3U10.00 0.10 0.40 0.70……0.00 1.00 1.00 1.00 1.00 ……0.20 0.00 0.00 1.00 1.00 ……(表中用9号字).….. …………………(空一行)Figure 1 Functions of……(9号字加黑居中)3 The Improved Method(空一行)3.1 Method one (二级标题10号字加黑)…………3.1.1 Discussing about method one (三级标题10号字)…………（空一行）3.2 Method two……………………(空一行)4 Conclusion (12号字加黑)…………(空一行)References (12号字加黑居中)（空一行）[1] M.Mizumoto,H.J.Zimmermann. comparision of fuzzy reasoning methods. Fuzzy Sets andSystems ,8(1982),p253～283 (参考文献均用10号字)国际会议论文排版要求及样张关于论文1.论文的书写顺序时:标题、作者姓名、作者单位，邮箱，摘要、关键词、引言、正文、结论、参考文献。
A fuzzy semantics for semantic web languages
A Fuzzy Semantics for Semantic Web LanguagesMauro Mazzieri1and Aldo Franco Dragoni21mauro.mazzieri@2Universit`a Politecnica delle MarcheDipartimento di Elettronica,Intelligenza Artificiale e Telecomunicazioni(DEIT)a.f.dragoni@univpm.itAbstract.Although the model-theoretic semantics of the languagesused in the Semantic Web are crisps,the need arise to extend themto represent fuzzy data,in the same way fuzzy logic extendfirst-order-logic.We will define a fuzzy counterpart of the RDF Model Theory forRDF(section2)and RDF Schema(section3).Last,we show how toimplement the extended semantics in inference rules(section4).Keywords:Fuzzy Logic,Knowledge Representation,Semantic Web,RDF, RDF Schema.1Knowledge representation on the webThe Semantic Web is an extension of the current web in which information is given well-defined meaning[1]by the use of knowledge representation(KR) languages.The KR languages used(RDF,RDF Schema and OWL)have the character-istics that make them useful on the web[2]:–the elements of the domain are represented by URI;–there is no global coherence requirements,as local sources can make asser-tions independently without affecting each other’s expressiveness.The languages have the ability to describe,albeit not formally,much more than their semantics can express.Their model theory captures only a formal no-tion of meaning,captured by inference rules;the exact‘meaning’of a statement can depend on many factors,not all accessible to machine processing[3].This feature can be useful to represent information fromfields that require knowl-edge representation paradigms other than the FOL-like RDF Model Theory or the expressive Description Logic used by OWL.Amongst those paradigms there is fuzzy logic,to represent vague or ambiguous knowledge.2Fuzzy RDFRDF has its own model-theoretic semantics,similar to that offirst-order logic. To represent fuzzy data,we will define a syntactic and semantic extension of RDF,similar to the extension fromfirst-order logic to fuzzy logic.Even if fuzzy data can be simply seen as a juxtaposition of a triple and a number,the model-theoretic approach has well-known theoretical advantages.We will try to be as plain as possible.Starting from RDF Syntax and RDF Model Theory,we will make as few changes as possible.In the rest of the paper, for the sake of brevity only the changes from RDF Semantics[3]are shown.2.1SyntaxThe RDF syntax must be extended to add to the triple subject,predicate, object a value.Such a value can be taken as a real number in the interval[0, 1],but every bounded real interval will do.This is not an extension from a3-elements tuple to a4-elements tuple as it may seem at afirst glance.The added element has a syntactic nature different from the others:it is not an element of the domain of the discourse,but a property related to the formalism used by the language to represent uncertainty and vagueness.The simple concrete syntax we define is as an extension of the EBNF of N-Triples as given in[4].Our extension is given in table1.N-Triples is a line-based,plain text format for encoding an RDF Graph,used for expressing RDF test cases.A statement has the form s p o.,where s,p and o are respectively the subject,the predicate and the object of the statement.Our extended syntax add an optional prefix n:to a statement in N-triple notation, where n is a decimal number representing the fuzzy truth-value of the triple. The use of decimal numbers instead of real numbers is only a limitation of the syntax and does not undermine the discussion.The term triple,used in the EBNF for N-Triple,is replaced with the more generic term statement.Triple and statement are often used in semantic web lit-erature as a synonym,but we prefer to use the latter to avoid confusion between a plain RDF statement(made actually of three parts)and a fuzzy RDF statement (that,although is still a triple semantically,is made up of four elements).The fuzzy value is defined as optional.This way,the syntax is backward-compatible;the intended semantics is that a statement with the form s p o.is equivalent to the statement1:s p o..With such a(syntactic only)default,we could take an inference engine implementing fuzzy RDF,let it parse plain RDF statements,and get the same results of a conventional RDF inference engine. Furthermore,as it would be clear in the description of fuzzy RDF inference rules (section4),even the complexity of the computation would be of the same order.We will not give an abstract syntax,nor a RDF/XML based syntax,as they would not be useful.It can be shown that all“physical“data(i.e.,data transmitted between host or processes)can be encoded using plain RDF reified statements.The extended syntax will be used only in the paper to write down the examples.fuzzyNtripleDoc::=line*line::=ws*(comment|statement)?eolncomment::=‘#’(character−(cr|lf))*statement::=(value ws+)?subject ws+predicate ws+object ws*‘.’ws* value::=1|0.[0–9]+subject::=uriref|nodeIDpredicate::=urirefobject::=uriref|nodeID|literaluriref::=‘<’absoluteURI‘>’nodeID::=‘_:’nameliteral::=langString|datatypeStringlangString::=‘"’string‘"’(‘@’language)?datatypeString::=‘"’string‘"’‘^^’urireflanguage::=[a–z]+(‘-’[a–z0–9]+)*encoding a language tag.ws::=space|tabeoln::=cr|lf|cr lfspace::=#x20/*US-ASCII space-decimal32*/cr::=#xD/*US-ASCII carriage return-decimal13*/lf::=#xA/*US-ASCII line feed-decimal10*/tab::=#x9/*US-ASCII horizontal tab-decimal9*/string::=character*(with escapes as defined in section Strings of[4]) name::=[A-Za-z][A–Za–z0–9]*absoluteURI::=character+(with escapes as defined in section URI Referencesof[4])character::=[#x20–#x7E]/*US-ASCII space to decimal126*/Table1.EBNF for Fuzzy N-Triples2.2Simple interpretationThe RDF Model Theory[3]is based on the concept of extension.An interpre-tation satisfies a triple s p o.if the couple formed by the interpretation of the subject and the interpretation of the object belongs to the extension of the interpretation of the property.In this fuzzy counterpart,a couple subject,object has a membership de-gree to the extension of the predicate,given by the number prepended to the statement.The extension is not an ordinary set of couples anymore,but a fuzzy set of couples.In other words,a fuzzy RDF interpretation satisfies a statement n:s p o.if the membership degree of the couple,formed by the interpreta-tion of the subject and the interpretation of the object,to the extension of the interpretation of the predicate,is greater or equal than n.We have chosen not to make the mapping between vocabulary items and domain fuzzy.Instead,the membership of a resource to the domain is fuzzy. This is a step which poses some theoretical problems,in particular when we have to deal with properties in simple interpretations.In RDF interpretation, the property domain IP is a subset of the resource domain IR,so in fuzzy RDFinterpretations would be enough to make IP a fuzzy subset of IR;in simple interpretations,instead,there is no formal relation between IP and IR,so when the mapping IS from URI references to(IR∪IP)becomes fuzzy we need a further device.The chosen solution is to define a domain IDP for properties, so that IP is a fuzzy subset of IDP,and to modify the definition of IS to a mapping URI references∈V→(IR∪IDP).RDF interpretations does not need IDP,as IP can be shown to be a fuzzy subset of IR.Definition of a simple interpretation A simple fuzzy interpretation I of a vocab-ulary V is defined by:1.A non empty set IR of resources,called the domain or universe of I2.A non empty set IDP,called the property domain of I3.A fuzzy subset IP of IDP,called the set of properties of I4.A fuzzy mapping IEXT:IP→2IR×IR,i.e.the fuzzy set of pairs x,ywith x,y∈IR.5.A mapping IS from URI references∈V→(IR∪IDP)6.A mapping IL from typed literals∈V→IR7.A distinguished subset LV⊆IR,called the set of literal values,which con-tains all the plain literals of VThe belonging of an element to the properties domain is strictly related to the use of such element as a property in a statement.Therefore,we have defined a membership degree to the property domain,intuitively related to the truth value of the statements in which the resource is used as a property.2.3Denotations for ground graphsThe next step is to define the semantic conditions an interpretation must satisfy in order to be a model for a graph.We state the semantic conditions that relate the membership degree of a couple subject,object to an extension and the truth of a fuzzy statement.We will use the abbreviated Zadeh’s notation A(x)=n,instead ofµA(x)=n, to state that the membership degree of the element x to the set A is equal to n[5].Semantic conditions for ground graphs–if E is a plain literal aaa∈V,then I(E)=aaa–if E is a plain literal aaa@ttt∈V,then I(E)= aaa,ttt–if E is a typed literal∈V,then I(E)=IL(E)–if E is a URI reference∈V,then I(E)=IS(E)–if E is a ground triple n:s p o.,then I(E)=true if s,p and o∈V, IP(I(p))≥n and IEXT(I(p))( I(s),I(o) )≥n,otherwise I(E)=false.–if E is a ground RDF graph,than I(E)=false if I(E )=false for some triple E ∈E,otherwise I(E)=trueOnly the condition of truth and falsity of a ground statement in the interpre-tation is affected.The given formulation of the condition has as a consequence that a graph where the same statement appears more than once,with differ-ent membership degrees,is equivalent to a graph where the statement appears only once,with a membership degree equal to the maximum of the membership degrees.Note that whether a statement is a model for a graph or not is not a fuzzy concept;it is either true or false.However,it could be interesting to compute the minimum and maximum membership degree to an extensions a couple must have in an interpretation to be a model of a given graph.This minimum degree has a role similar to the degree of truth of a statement in a knowledge base. 2.4Simple entailmentThe definition of simple interpretation is not affected.A set S of RDF graphs (simply)entails a graph E if every interpretation which satisfies every member of S also satisfies E.Given a graph G and a triple s,p,o ,it could be interesting to compute the minimum and maximum value of n such that G entails n:s p o..Those bounds must be taken in account when we compute the deductive closure of the graph,as it is not unique.Section2of RDF Semantics[3]shows many lemmas that apply to simple interpretations.All of them retain their validity within fuzzy RDF Model Theory, making some adjustments in the proof of some of them.We will show these.The Empty Graph Lemma can be shown using the same proof.The definition of an empty graph is the same as in plain RDF:an empty graph is a graph with no statements at all.It is important to note that an empty graph can not be defined as a graph with no not-zero-valued statements.Statements such as0:s p o., although pretty useless,cannot be ignored,as the semantic requirement that s, p and o must belong to the graph’s vocabulary still applies.Subgraph Lemma,Instance Lemma and Merging Lemma retain both their validity and their proofs with the new semantics.Interpolation Lemma,Anonymity Lemma,Monotonicity Lemma and Com-pactness Lemma make use in their proof of a way of constructing an interpre-tation of a graph using lexical items in the graph itself,the so called Herbrand interpretation[6].To prove the lemmas,we need to construct a similar interpre-tation for a fuzzy graph.The(simple)Herbrand fuzzy interpretation of G,written Herb(G),can be defined as follows.–LV Herb(G)is the set of all plain literals in G;–IR Herb(G)is the set of all names and blank nodes which occur in subject or object position of statements in G;–IDP Herb(G)is the set of URI references which occur in the property position of statements in G;–IP Herb(G)(p)is the maximum of n for all statements in which p occur in property position;–IEXT Herb(G)( s,o )is the maximum n for all the statements n:s p o.in G–IS Herb(G)and IL Herb(G)are both identity mappings on the appropriate parts of the vocabulary of G.Using this definition of Herbrand interpretation instead of that in AppendixA of[3],the proofs for cited lemmas still apply.2.5RDF InterpretationRDF Semantic Conditions–IP(x)=IEXT(I(rdf:type))( x,I(rdf:Property))–If”xxx”∧∧rdf:XMLLiteral∈V and xxx is a well-typed XML literal string, then•IL(”xxx”∧∧rdf:XMLLiteral)is the XML value of xxx;•IL(”xxx”∧∧rdf:XMLLiteral)∈LV;•IEXT(I(rdf:type))( IL(”xxx”∧∧rdf:XMLLiteral),I(rdf:XMLLiteral) )=1–If”xxx”∧∧rdf:XMLLiteral∈V and xxx is an ill-typed XML literal string, then•IL(”xxx”∧∧rdf:XMLLiteral)/∈LV;•IEXT(I(rdf:type))( IL(”xxx”∧∧rdf:XMLLiteral),I(rdf:XMLLiteral) )=0Thefirst RDF semantic condition has the consequence that IP must be a subset of IR.Given such a fact,there is no more need of IDP,as it was for simple interpretation.IP can be directly defined as a fuzzy subset of IR.The second and third conditions equal to see the well-formedness of an XML Literal as crisp truth-valued.We could conceive an external machinery that can be considered completely trustworthy as it classify an XML literal as well-formed or not.RDF axiomatic triples By definition,we give axiomatic triples a unit truth value.Given the(syntactic)convention that a triple s p o.is equivalent to the fuzzy statement1:s p o.,we can take the table of axiomatic triples of RDF in section3.1of[3]and copy it as-is as the table of axiomatic statements of fuzzy RDF.3Fuzzy RDF SchemaThe path from RDF Schema to Fuzzy RDF Schema follows the same guidelines of the previous section.The RDFS semantics is conveniently stated in terms of a new semantic con-struct:the class[3].A class is a resource with a class extension,ICEXT,which represents a set of things in the universe which all have that class as the object of their rdf:type property.Thus,the definition of a class roots in the definition of extension.In fuzzy RDF,extensions are fuzzy set of couples;in fuzzy RDFS,class extensions are fuzzy sets of domain’s elements.3.1RDFS InterpretationA RDFS interpretation define the domains for resources(IR),literals(IL)and literal values(LV)in terms of classes.In fuzzy RDFS they are fuzzy subdomains of IR.We will give RDFS semantic conditions and axiomatic triples,then we will try to explain the more problematic definitions:domains/ranges(section3.2) and subproperties/subclasses(section3.3).RDFS semantic conditions–ICEXT(y)(x)=IEXT(I(rdf:type))( x,y )•IC=ICEXT(I(rdfs:Class))•IR=ICEXT(I(rdfs:Resource))•IL=ICEXT(I(rdfs:Literal))–ICEXT(y)(u)≥min(IEXT(I(rdfs:domain))( x,y ),IEXT(x)( u,v ))–ICEXT(y)(u)≥min(IEXT(I(rdfs:range))( x,y ),IEXT(x)( u,v ))–IEXT(I(rdfs:subPropertyOf))is transitive and reflexive on IP–If IEXT(rdfs:subPropertyOf)( x,y )=n,then IP(x)≥n,IP(y)≥n, min a,b {1−IEXT(x)( a,b )+IEXT(y)( a,b )}≥n–IEXT(I(rdfs:subClassOf))( x,I(rdfs:Resource) )=IC(x)–If IEXT(rdfs:subClassOf)( x,y )=n,then IC(x)≥n,IC(y)≥n, min a{1−IC(x)(a)+IC(y)(a)}≥n.–IEXT(I(rdfs:subClassOf))is transitive and reflexive on IC–IEXT(I(rdfs:subPropertyOf))( x,I(rdfs:member) )=ICEXT(I(rdfs:ContainerMembershipProperty))(x)–ICEXT(I(rdfs:Datatype))(x)=IEXT(I(rdfs:subClassOf))( x,I(rdfs:Literal) )RDFS axiomatic triples As for RDF axiomatic triples,fuzzy RDFS axioms are the same of plain RDFS,from section4.2of RDF Semantics[3].3.2Domains and rangesThe semantic condition on domains looks quite complicated.To explain it,we will proceed by grades.In plain RDF Schema,if x,y ∈IEXT(I(rdfs:domain))and u,v ∈IEXT(x)then u∈ICEXT(y).In fuzzy set theory,let R be a fuzzy relation on X×Y.Then the domain is defined as dom(R)(x)=sup y R(x,y)[7],i.e.the least upper bound of R(x,y) for all y.In fuzzy RDFS,we have to deal both with a fuzzy notion of domain,and with a fuzzy assignment of a domain to a property.Let consider a resource u and a class y.For each property x,we take the mini-mum between IEXT(I(rdfs:domain))( x,y )and IEXT(x)( u,v ).Then,fol-lowing the original RDFS condition,ICEXT(y)(u)must be greater or equal than this value.The previous condition must hold for every property x,so it’s equivalent to state that must be taken the maximum value.The conditions for ranges are analogous.3.3Subproperties and subclassesSubproperties and subclasses are fully analogous concepts.The set inclusion is between extensions for the former,between class extensions of the latter.To define the semantics of subClassOf and subPropertyOf,we need a rela-tion of set inclusion between fuzzy sets that takes into account also the degree of the relation of inclusion itself.This relation must be transitive and reflexive.Zadeh’s definition of fuzzy subset[8]3(A⊆B⇐⇒∀x∈X A(x)≤B(x)) is transitive and reflexive,but is not a fuzzy relation:either the set A is a subset of B,or not.What we need is instead a weaker fuzzy subset relation;a relation that reduces to the Zadeh’s one when the subclass/subproperty relation has a unit truth value.It must also maintain the reflexivity and transitivity properties.Dubois and Prade[7]define weak inclusion αasA αB⇐⇒x∈(A∪B)α∀x∈X,whereαis a parameter and(·)αis theα-cut4.This relation is transitive only forα>12.Other definitions of weak inclusion make use of inclusion grades.An inclusion grade I(A,B)is a scalar measure of the inclusion of the set A in the set B. In general,A⊆αB iffI(A,B)≥α,where⊆αdenote a weak inclusion with inclusion gradeα.We have chosen to use the inclusion grade defined as[7]:3Again,we use the abbreviation A(x)for the membership functionµA (x).4Theα-cut Aαof A is the set of all elements with a membership value to A greater thanα,withα∈(0,1]Aα={x|A(x)≥α}I(A,B)=inf x∈X x)where inf is the infimum and|−|is the bounded difference5.When A⊆B,I(A,B)=1[7].This inclusion grade could also be written as I(A,B)=inf x∈X(1−max(0,A(x)−B(x)))=inf x∈X min(1,(1−A+B)).Furthermore,let’s suppose that there is at least an x such that A(x)>B(x). Then I(A,B)could be written as inf x∈X(1−A+B).The semantic condition requires such measure to be greater than or equal to n,where n is the truth value of the statement.In this case the semantic condition reduces toinf x∈X(1−A+B)≥n.It could be interesting to ask how much this definition differs from the con-dition for classical fuzzy subsets,A(x)≤B(x).If A⊆B,then I(A,B)=1,so the semantic condition holds for any n∈[0,1].Let’s call d(x)the difference d(x)=A(x)−B(x),so that1−A+B=1−d.We suppose that there is at least an x such that A(x)>B(x),so d(x)has at least a positive value.The semantic condition could then be written inf x∈X(1−d(x))≥n.The maximum positive value of the difference d equal to1−n.As n is the truth value of the statement that asserts the relation of subprop-erty or subclass,and1−n represent the lack of truth of the same statement,we can conclude that the maximum allowable positive difference between A(x)andB(x)is equal to the lack of truth on the subproperty or subclass relation.4Fuzzy RDF entailment rulesRDF Model Theory’s entailment rules[3]are all of the same form:add a state-ment to a graph when it contains triples conforming to a pattern.Each rule has only one or two antecedent statements and derive only one new inferred statement;either P R or P,Q R.Given the way fuzzy RDF semantics is defined,the corresponding inference rules for fuzzy RDF are analogous;only the fuzzy truth values of inferred state-ments must be computed.The simplest possible choice that respect the semantics is:–With rules as P Q,having only one antecedent,the truth value of the consequent Q is taken to be the same of the antecedent P.–With rules as P,Q R,the truth value of R is the minimum between the truth values of P and Q.The inference rules for RDF/RDFS are shown in table2.They were derived from the rules used by the Sesame[10]forward-chaining inferencer.5∀x∈X,(A|−|B)(x)=max(0,A(x)−B(x))[9]Sesame is a generic architecture for storing and querying RDF and RDF Schema.It makes use of a forward-chaining inferencer to compute and store the closure of its knowledge base whenever a transaction adds data to the reposi-tory[11].Sesame applies RDF-MT inference rules in a optimized way,making use of the dependencies between them to eliminate most redundant inferencing steps.To obtain a fuzzy RDF storage and inference tool it is only a matter of modify Sesame RDF-MT inferencer,making it compute the correct truth values for inferred statements,and to extend the underlying storage to make room for a truth value(i.e.,a number)for each statement.This shows how a simple proof-of-concept fuzzy RDF inferencer is easy to implement.The starting point is the code base of an inference engine that im-plements the RDF model theory.It can be shown that an inference engine implementing such rules is correct: all its rules are valid,in the sense that a graph entails any larger graph that is obtained by applying the rules to the original graph.There is no formal proof that it is also complete,but there is not such a proof for plain RDF Model Theory inference rules either.References1.Hendler,J.,Lassila,O.,Berners-Lee,T.:The semantic web.Scientific American(2001)28–372.Berners-Lee,T.:What the semantic web can represent.W3C design issues,WorldWide Web Consortium(September1998)3.Hayes,P.:RDF Semantics.W3C recommendation,World Wide Web Consortium(10February2004)4.Grant,J.,Beckett,D.:RDF test cases.W3C recommendation,World Wide WebConsortium(2004)/TR/rdf-testcases/.5.Zadeh,L.A.:A fuzzy set theoretic interpretation of linguistic hedges.Journal ofCybernetics2(1972)4–346.Goldfarb,W.D.,ed.:Logical Writings of Jacques Herbrand.Harvard UniversityPress,Cambridge(1971)7.Dubuois,D.,Prade,H.:Fuzzy sets and Systems.Academic Press,New York,NJ(1980)8.Zadeh,L.A.:Fuzzy rmation and Control(1965)338–3539.Zadeh,L.A.:Calculus of Fuzzy Restrictions.In:Fuzzy Sets and Their Applicationsto Cognitive and Decision Processes.Academic Press,New York(1975)1–39 10.Broekstra,J.,Kampman,A.,van Harmelen,F.:Sesame:A generic architecturefor storing and quering RDF and RDF Schema.In Horrocks,I.,Handler,J.,eds.: Proceedings of thefirst International Semantic Web Conference(ISWC2002), Sardinia,Italy(2002)54–6811.Broekstra,J.,Kampman,A.:Inferencing and truth maintenance in RDF Schema:exploring a naive practical approach.In:Workshop on Practical and Scalable Semantic Systems(PSSS)2003.Second International Semantic Web Conference (ISWC),Sanibel Island,Florida,USA(2003)#antecedents consequent1iii:xxx aaa yyy iii:aaa rdf:type rdf:Property2.1iii:xxx aaa yyy kkk:xxx rdf:type zzzjjj:aaa rdfs:domain zzz where kkk=min(iii,jjj)2.2iii:aaa rdfs:domain zzz kkk:xxx rdf:type zzzjjj:xxx aaa yyy where kkk=min(iii,jjj)3.1iii:xxx aaa uuu kkk:uuu rdf:type zzzjjj:aaa rdfs:range zzz where kkk=min(iii,jjj)3.2iii:aaa rdfs:range zzz kkk:uuu rdf:type zzzjjj:xxx aaa uuu where kkk=min(iii,jjj)4a iii:xxx aaa yyy jjj:xxx rdf:type rdfs:Resource4b iii:xxx aaa uuu iii:uuu rdf:type rdfs:Resource5a.1iii:aaa rdfs:subPropertyOf bbb kkk:aaa rdfs:subPropertyOf cccjjj:bbb rdfs:subPropertyOf ccc where kkk=min(iii,jjj)5a.2iii:bbb rdfs:subPropertyOf ccc kkk:aaa rdfs:subPropertyOf cccjjj:aaa rdfs:subPropertyOf bbb where kkk=min(iii,jjj)5b iii:xxx rdf:type rdf:Property iii:xxx rdfs:subPropertyOf xxxreflexivity of rdfs:subPropertyOf6.1iii:xxx aaa yyy kkk:xxx bbb yyyjjj:aaa rdfs:subPropertyOf bbb where kkk=min(iii,jjj)6.2iii:aaa rdfs:subPropertyOf bbb kkk:xxx bbb yyyjjj:xxx aaa yyy where kkk=min(iii,jjj)7a iii:xxx rdf:type rdfs:Class iii:xxx rdfs:subClassOf rdfs:Resource7b iii:xxx rdf:type rdfs:Class iii:xxx rdfs:subClassOf xxxreflexivity of rdfs:subClassOf8.1iii:xxx rdfs:subClassOf yyy kkk:xxx rdfs:subClassOf zzzjjj:yyy rdfs:subClassOf zzz where kkk=min(iii,jjj)8.2iii:yyy rdfs:subClassOf zzz kkk:xxx rdfs:subClassOf zzzjjj:xxx rdfs:subClassOf yyy where kkk=min(iii,jjj)9.1iii:xxx rdfs:subClassOf yyy kkk:aaa rdf:type yyyjjj:aaa rdf:type xxx where kkk=min(iii,jjj)9.2iii:aaa rdf:type xxx kkk:aaa rdf:type yyyjjj:xxx rdfs:subClassOf yyy where kkk=min(iii,jjj)10iii:xxx rdf:type iii:xxx rdfs:subPropertyOf rdfs:member rdfs:ContainerMembershipProperty11iii:xxx rdf:type rdfs:Datatype jjj:xxx rdfs:subClassOf rdfs:LiteralX1iii:xxx rdf:_*yyy jjj:rdf:_*rdf:type rdfs:ContainerMembershipPropertyThis is an extra rule for list membershipproperties(_1,_2,_3,...).The RDF MTdoes not specify a production for this.Table2.Fuzzy RDF inference rules。
fuzzy language 论文(模糊语)
Analysis of the Specific Pragmatic Functions of Fuzzy Language in theBusiness English【Abstract】As English for specific purpose (ESP), business English should be accurate and strict, in the pursuit of conciseness，clearness，accuracy. However，fuzzy language is still widely used in every aspect of business English. In this article，it mainly focuses on the specific analysis of several pragmatic functions in which it specifically analyses the positive pragmatic functions in the business English.【Key words】fuzzy language；business English；pragmatic function1.Topic SelectionTo my way of thinking，fuzzy language, whose role is essential and active in current days, especially in business occasions, is full of charm. Therefore, I look up a variety of materials and references related to the pragmatic functions in the business English.2.The Contents2.1 Introduction of the BackgroundAs a common strategy used in business, a good mastery of the pragmatic functions of fuzzy language is the general trend of the times. As English for specific purpose (ESP), business English should be accurate, strict, and be in the pursuit of conciseness，clearness，accuracy. However，fuzzy language is still widely used in every aspect of business English and the pragmatic functions of it are wildly focused.2.2 An Overview of Fuzzy Language and Pragmatic FunctionsThe concept of vagueness was first proposed by the American professor Zadeh (1965) in his article entitled Fuzzy Sets. He pointed out that if the expression is ambiguous and lack of accurate boundary to its opposite, we could collectively call them fuzzy language. Bertrand Russell (1923) also said that all of the languages are more or less fuzzy. Fuzzy language exists anywhere, because lots of words are full of uncertainty, such as beautiful, somewhat, as if and so forth. Pragmatic function is the language effects reflected from linguistic form which is suitable for communication environment in the situated context. It has a close connection with the context. If the context is different, the same words may show different pragmatic functions. Here, the pragmatic function refers to the language effects in the fuzzy language.2.3 Analysis of specific Pragmatic Functions of Fuzzy Language in the Business EnglishDuring the real business English communication, as a special purpose, business English is widely used in business letters, business negotiation, business contract, etc. In vague language, Channell (1994) mentioned that it is no concerned about good or bad fuzzy language, the key lies in whether it is used correctly. Li Ming (2004) said:" Business English language tends to aim at objectivity, accuracy, and plainness. "Of course, business English should be accurate and rigorous, however, the appropriate use of fuzzy language can make it more vivid, flexible, image and accurate. If the speaker is not willing or unnecessary to clarify clearly, the fair use of fuzzy language will reduce face threatening acts, softens conversation and protects self-image, etc.2.3.1 Fuzzy language will improve the implication and politeness of expressionMr. Brown (1987) thought the best way to express the politeness is to use fuzzy language while expressing something rude or threatening. In business communication, such as business letters, it is essential to use fuzzy language for the sake of showing sort of politeness and implication. Fuzzy expressions can achieve the effect of implicitness and indirectness, especially when it comes to some sensitive topics, which definitely benefits people on both ends of the transaction.Example 1Much as we would like to avail ourselves of the offer made to us, we find it impossible to accept owing to your price being about 10% higher than the average.The phrase "about 10% higher than the average" is a good illustration of their politeness and implication. It seemingly state the fact, however, the speaker is trying to convey the information that they just cannot accept price offered higher than their forecast, so they decline the price in a relatively polite and implicit manner without directly pointing out. And the word "much" shows their desire to successfully cooperate, which may leave some room for future counter offer.2.3.2 Fuzzy language will improve the efficiency, and flexibility of expression.It is necessary for people to leave some room for both parties in order to form a better cooperation. At that time, fuzzy language comes in handy, the effect of flexibility mainly reflected in the derivative meaning of the words. Especially during the process of negotiation, the flexibility of expression is perfectly performed because the negotiatorshould be free to use the fuzzy language strategy and be good at applying the negotiation skills so that he can achieve his purpose.Example 2:As you may notice, the price for pork has gone up currently. Our price is attractive as compared with other supplier.In this sentence, it uses "attractive" instead of showing a clear price to convey some ambiguous information on price, which maintains flexibility in the offer, avoiding the other party thinking of absolute terms. Then the words "other supplier" do not clearly indicate anyone, but it improves the efficiency of tempting the other party to accept offer. With such an intelligent approach, the negotiators try to be objective while highlighting their own advantages.2.3.3 Fuzzy language will be convey the information more accurately and show the objective accuracyOn the surface, fuzzy language is full of uncertainty, but it also embodies in some accurate context. To some extent, fuzzy words convey more sufficient information that may be more precise than non-fuzzy words, showing a strategy of using fuzziness to convey some precise information. In this way, the expression reflects sort of the strictness and objectiveness.Example 3In general, our payment terms are by irrevocable letter of credit at sight and we do notaccept D/P term."In general" is a phrase to summary one's opinion on something. However, it does not clarify what "In general" exactly refers to, and the range, degree and limits are all uncertain. It shows a more strict and accurate meaning, meanwhile, it ease the communication atmosphere, driving the other party understand and accept easily than before2.3.4 Fuzzy language will be a perfect way to one's self-protectionDuring business communication, especially some commerce trade, the using of fuzzy language improve the freedom of expression, avoid the absoluteness, and gain the initiative. In particular, if the partner wants one side to take the responsibility, or one offers something wrong to his client, even causes the economic losses, fuzzy language help one easier to protect himself.Example 4The seller is not to take the responsibility for no delivery or shipment delay due to generally irresistible reason."generally irresistible reason" is the sort of a perfect strategy using in the business contract. It points out that the seller is irresponsible if the shipment is delayed by some objective factors, such as worker strike, unpredictable weather, etc. The writing person avoids their own responsibility in a tactful way, and subtly self-protects his own party if both parties have a conflict in the shipment issue.2.4 ConclusionBased on the above analysis of specific pragmatic functions of fuzzy language in thebusiness English, it is easy to find out that fuzzy language is a kind of language strategy adopted by people for the sake of communication in some particular business occasions. In different occasions, fuzzy language plays different roles in the pragmatic functions. For instance, the pragmatic functions in business letters, business negotiation, business contract, business advertising and so forth, play a crucial role in the international business communication. However, it's worth noting that under some certain circumstance, fuzzy language has limited and negative effect so that it should be used properly according to the specific context. Only used in a correct and appropriated way, can fuzzy language promote international business activities smoothly.3. MethodologyBy consulting the teacher, I focus on four specific pragmatic functions of fuzzy language in the business English. On the whole, I collect a number of relative materials and references of this topic, then state the theoretical knowledge and background of the topic. After presenting relative information of every pragmatic function, I cite examples to respectively analyze the different positive functions for the sake of the combination of the theory and argument. At last, I draw a conclusion to the overall essay.References[1]Bertrand Russell. V agueness[J]. The Australian Journal of Psychology and Philosophy, 1923（3）：84-92.[3]Brieger, N．The Teaching Business English Handbook[M].New York: New York Associates Publications, 1997.[3]Channell, J. Vague Language[M]. Oxford: Oxford University Press, 1994: 165- 195.[4]Lakoff,G.Hedges.A Study in Meaning criteria andthe logic of fuzzy concept[J].Journal of Philosophical Logic,1973(2).[5]Zadeh, L.A.Fuzzy Sets[M]．Information and Control,1965(8): 338～353.[6]程同春. 论模糊语言在国际商务英语中的语用功能[J]. 国际经贸探索，2000(5).[7]李明. 论商务英语的语言特点和语篇特点[J].广东外语外贸大学学报, 2004(4): 32～37.[8]刘晓娟，王红梅. 论模糊语用策略在商务英语中的运用[J]. 商场现代化，2007[9]韩晓方, 温美昕. 国际商务信函中模糊语言的语用分析[ J]. 商业现代化, 2007( 5)[10]乔娇. 国际商务英语中的模糊语言语用功能探讨[J]. 河北北方学院学报，2008(6)[11]邱天河. 语用策略在国际商务谈判中的运用[J]．外语与外语教学, 2000(4)[12]孙全军. 模糊语言在英文商务信函写作中的作用[J]. 考试周刊，2008[13]吴建新.《模糊语言在外贸谈判中的作用》，《现代外语》1990(4)[14]吕昊, 刘显正, 罗萍编著. 商务合同写作及翻译[ M] . 武汉: 武汉大学出版社, 2005(1)[15]张乔.《模糊语义学》[Ｍ].北京:中国社会科学出版社, 1998。
常见英语同近义词(同类词)152组
常见英语同/近义词（同类词）152组1.选择choose, elect, select，pick.2.方式/法way, method, means，manner，approach, mode, system，tactics 3。
semester，term 4。
determine，decide 5.学分;成绩credit, score, mark, grade 6。
满意的content，satisfied 7。
停止；放弃stop，halt, desist，cease，drop out，withdraw，retreat，quit, resign，give up，surrender, depart, abandon，desert, discard，forsake; retire, retreat，reject, recess 8。
调查survey，investigate, explore, inspect, look into，probe, observe, review，study 9。
改变;变化change，alter(alert），convert，modify, transform, shift, alternate, vary 10.修订/改revise, change，modify, alter，mend, rectify，correct, amend，emend 校订/正11.珍爱/惜/重cherish, treasure, worship，appreciate, value 12.包括/含include, contain, compose，comprise, involve, cover，encompass 13.分发distribute, give out, pass out 14。
提供provide, give, furnish, offer，supply 15。
风景scene, scenery, sight，view, landscape, outlook 16。
日语电脑相关词汇
电脑相关词汇2009-08-20 16:16アアーキテクチャ「architecture」结构アイコン「icon」图标アイテム「item」项目アイドルタイム「idle time」空闲时间あいまい検索「fuzzy matching」模糊（匹配）检索アウトソーシング「out-sourcing」委外服务アウトプット（する）「output」输出アウトライン？フォント「outline font」轮廓字体アカウント「account」账号アクセサリ「accessory」附件アクセス（する）「access」（インターネットへ/internet）上网（回線などに）接入（サイトへ/site）访问（記憶装置へ/memory drive）存取アクセスカウンタ（ー）「access counter」访问计数器アクセス権「access right」存取权アクセスポイント「access point」接入点アクセスナンバー「access number」访问号アクセス優先度「access priority」存取优先级アクセスログ「access log」访问日志アクティブ「active」活动アクティブにする「activate」激活アクティブ。
ファイル「active file」当前文件アクティブ？フィルター「active filter」有源滤波器アクティブ。
ウィンドウ「active window」活动窗口アクティブ。
デスクトップ「active desktop」活动桌面アスキーコード「American Standard Code for Information Interchange」 ASCII 美国信息互换标准代码アステリスク「asterisk」星号「アスロン」？商標？「Athlon」速龙アスペクト比「aspect ratio」纵横比アセンブラ「assembler」汇编程序アセンブリー言語「assembly language」汇编语言アタッチ「attach」配属アダプタ「adapter」适配器アッテネータ「attenuator」衰减器アップ「up」アップグレード「upgrade」升级アップデート「update」更新アップロード「upload」上传/アップローディング「up-loading」アドレス「address」地址アドレス帳「address book」通讯簿アナログ「analog」模拟アナログ？ディジタル変換器「analog-digital converter」模拟-数字转换器アナログ出力「analog output」模拟输出アニメーション「animation」动画アプリケーション「application」应用软件アプリケーションプログラム「application program」应用程序アプリケーションの追加と削除「add or remove programs」添加/删除程序アプレット「applet」小程序アルゴリズム「algorithm」算法アンインストール「uninstall」卸载アンカー「anchor」固定アンチウィルスソフト「antivirus software」杀毒软件イイーサネット「Ethernet」以太网イジェクトボタン「eject button」弹出按钮イタリック「italic」斜体イベント「event」项目イベントドリブンプログラミング「event-driven programming」事件驱动的程序设计イニシャル「initial」最初的イメージ「image」图象イメージマップ「image map」图像地图イメージスキャナー「image scanner」图像扫描仪インクカートリッジ「ink cartridge」墨盒インクジェットプリンタ「ink-jet printer」喷墨打印机インクリボン「ink ribbon」色带インクリメント「increment」增加インサート。
专八Synonym
74.expound explain elaborate
75.rustic(有农村特色的)
76.obesity corpulence chubby flabby（肌肉松垂肥胖的floppy） fat obese stout corpulent
- lean skinnythin slim slender
-awkward clumsy stiff
15.uncompromising firm -flexible
16.denizen citizen inhabitant
17.unrelenting (-intermittent fitful peropdic) unremitting uninterrupt remorseless constant(constancy steadiness) unremitting continual continuous unceasing persistent
66.tangent(切线，切面；正切函数)
67.dote(过分喜爱，溺爱) indulge spoil
68.flux(continuous change or succession of changes,unsettled state) alteration -rest stability
impoliteness rudeness discourtesy
40.礼节 etiquette; courtesy; manners; ceremony; proprieties
礼乐射御书数
rites, music, archery, driving a chariot, learning and mathematics (known together as "six arts", which ancient scholars were required to master)
学术英语范文outline
学术英语范文outlineIntroductionThe ability to effectively communicate in academic settings is a crucial skill for students and professionals alike. Academic English, also known as scholarly or scientific English, refers to the specific language conventions and writing styles used in formal educational and research contexts. This style of writing is characterized by its objectivity, precision, and adherence to established conventions. Mastering academic English is essential for success in higher education, research, and various professional fields that require the presentation of complex ideas and findings. This essay will explore the key elements of academic English, including its linguistic features, organizational structure, and the importance of developing proficiency in this specialized form of communication.Linguistic Features of Academic EnglishOne of the defining features of academic English is its formal and objective tone. This is achieved through the use of complex sentence structures, precise vocabulary, and an impersonal writing style. Academic writing typically avoids the use of contractions,colloquialisms, and personal pronouns, instead favoring more formal language choices. For example, a sentence such as "The researchers found that the new treatment was effective" would be preferred over a more casual phrasing like "They found the treatment worked well."Additionally, academic English is characterized by the use of discipline-specific terminology and technical jargon. This specialized vocabulary is essential for accurately conveying complex ideas and concepts within a particular field of study. Researchers and scholars must be able to use these terms correctly and consistently to ensure clear and unambiguous communication with their peers.Another linguistic aspect of academic English is the emphasis on precise and concise language. Academic writers strive to express their ideas clearly and succinctly, avoiding unnecessary wordiness or redundancy. This is achieved through the use of well-structured sentences, logical transitions, and a focus on conveying the key points and findings.Organizational Structure of Academic WritingThe organizational structure of academic writing is another crucial element that distinguishes it from other forms of communication. Academic essays and research papers typically follow a standardized format, which includes an introduction, body, and conclusion. The introduction serves to provide background information, establish thecontext, and clearly state the thesis or main argument. The body of the text then presents the supporting evidence, analysis, and discussion of the topic, often organized into well-defined sections or paragraphs. Finally, the conclusion summarizes the key points, draws logical inferences, and may suggest avenues for further research or exploration.Within this overall structure, academic writers also employ various rhetorical strategies to effectively convey their ideas. These strategies may include the use of definitions, explanations, comparisons, and the integration of relevant sources and citations. The inclusion of in-text citations and a comprehensive reference list is a hallmark of academic writing, as it demonstrates the writer's engagement with the existing scholarly discourse and their ability to properly acknowledge the work of others.Importance of Proficiency in Academic EnglishDeveloping proficiency in academic English is essential for success in higher education and various professional contexts. For students, the ability to write in the academic style is crucial for completing assignments, research papers, and thesis or dissertation projects. Mastering academic English allows students to effectively communicate their ideas, demonstrate their understanding of course material, and engage with scholarly sources in a meaningful way.Beyond the academic realm, proficiency in academic English is also highly valued in many professional fields, such as scientific research, medicine, law, and business. In these contexts, the ability to present complex information, data, and findings in a clear, concise, and well-organized manner is crucial for effective communication with colleagues, clients, and stakeholders. Professionals who can navigate the conventions of academic English are often better equipped to contribute to the knowledge base of their respective fields and participate in the broader scholarly discourse.Moreover, the globalization of education and research has further emphasized the importance of academic English proficiency. With the increasing internationalization of higher education and the growing prominence of English as the lingua franca of academia, the ability to communicate effectively in academic English has become a valuable asset for scholars, researchers, and students from diverse cultural and linguistic backgrounds.ConclusionIn conclusion, academic English is a specialized form of communication that is essential for success in higher education and various professional contexts. Its linguistic features, such as formal tone, precise vocabulary, and complex sentence structures, as well as its standardized organizational structure, set it apart from other forms of written expression. Developing proficiency in academicEnglish is crucial for students and professionals alike, as it enables them to effectively convey complex ideas, engage with scholarly sources, and contribute to the knowledge base of their respective fields. As the global academic landscape continues to evolve, the mastery of academic English will only become more vital for those seeking to thrive in the world of research, education, and professional advancement.。
日语整理版
217终了オプション关闭计算机261セットアップ（する）设定置setup218出力（する）输出output262セル（EXCEL)单元格cell219常時接続无限上网unlimited access263全角/半角切り替えキー全角/半角切264全角文字全角字符double-byte charactor308ディレクトリ目录directory 265センタリング居中centering309データ数据data266送信（する）发送send310データベース数据库database 267送信済みアイテム已发送邮件311手書き入力手写输入freehand 268ソースコード源代source code312テキスト文本text269ソースプログラム源程序source program313テキストファイル文本文件text file 270ソート排序sort314テキストボックス文本框text box 271ソケット插座socket315デジタル化数字化digitaliz 272ソフト(ウェア)软件software316デスクトップ桌面desktop 273ソフトウェア・パッケージ软件包software package317デスクトップパソコン台式机desktop 274ソリューション解決方案solution318デバイス设定備device 275ターミナル终端terminal319デバイスドライバ设备駆动程device 276ターミナルアダプタ终端適配器terminal adaptor320デバイスマネージャー设备管理器device 277ダイアログボックス对话框dialog box321デバッグ调试debugging 278タイトルバー标题栏title bar322糾錯279ダイナミック动态dynamic323除錯280ダイヤルアップ接続（す拨号上网dial-up connection324デフォルト黙認（値）default 281ダイヤルアップネットワー拨号网絡dial-up network325缺省282ダウン（する）当机down326デフラグ砕片整理デフラグ283ダウンロード（する）下载download; DL327テレビ会議电視会议video284タグ标记tag328テレビ电话可視电话video285タスクバー任务栏task bar329テンキー数字键盘ten key 286立ち上げ（る）启动boot330小键盘287タッチスクリーン触模屏touch screen331电源を入れる打开电源 switch 288タッチパネル触摸屏touch panel332开机289タッチパッド触摸板touch pad333电源を切る关闭电源 switch 290タッチペン触筆touch pen334关机291タブtab335电子メール电子邮件e-mail 292タブキー制表键tab key336伊妹儿293 Tab键337电邮294ダブルクリック（する）双击double click338伝送（する）伝输transmiss 295端末终端terminal339転送（する）伝输transfer 296チェックボックス复選框check box340（メールを）転送（す转发forward 297チャット聊天chat341添付ファイル附件attachmen 298チャネル信道channel342閉じる关闭close299ツールバー工具栏tool bar343閉じるボタン关闭按钮close300ツールボックス工具箱tool box344ドキュメント文档document 301次へ下一歩next345ドットマトリクス点陣dot302ディスク磁盘disk346トップ（页の）页首page top 303ディスクアレイ陣列盘disk array347トップページ首页top page304ディスクの空き領域可用磁盘空间remaining capacity?348ドメイン名域名dmain305ディスクの最適化磁盘碎片整理defragmentation349ドライバ駆动程序driver 306ディスク容量磁盘容量disk capacity350ドライブ駆动器drive307ディスプレイ显示器display351トラック磁道track352トラブルシューティング問題解答trouble shooting396バンドル・ソフト梱[糸邦]软bundled 353取り消し取消cancel397左クリック（する）左击left354内蔵内置internal398ビデオ会議視頻会議355内蔵399开く打开356名前を付けて保存另存為save as400ピンイン入力法全併输入法357入力（する）输入input401ファイル文件file358キー入力（する））键入key-in402ファックス伝真facsimile 359ノートパソコン笔记本电脑notebook computer；403ファジー模糊fuzzy360残り容量剰余容量remaining capacity404ファンクションキー功能键function 361バージョン版本version405フィルタ过滤器filter 362パージョンアップ（する）升級version up406ブート启动boot363バーチャル虚擬407フォーマット（する）格式化format 364パーティション分区408フォーラム论坛forum365ハード（ウェア）硬件hardware409フォルダ文件夾folder 366ハードコピー硬拷贝hard copy410フォント字体font367ハードディスク硬盘hard disk411不正アクセス非授権访问unauthori 368ハイパーテキスト超文本412ブック（EXCEL)工作簿book369ハイパーメディア超媒体413フッター页脚footer 370ハイパーリンク超链接414太字体（ボールド）粗体bold type 371バグ错误415ブラウザ浏览器browser 372臭虫416プラグイン插件373パケット分组417ブラックボックス黒匣子black box 374数据报418プラットホーム平台375信息包419フリーソフト免费软件free376パケット通信分组通信420プリンタ打印机printer 377パス路径path421プルダウンメニュー下拉菜单pulldown 378パスワード口令password422プレインストール预装preinstal 379パソコン电脑PC423フレーム框架frame380パソコンデスク电脑桌424プレビュー预浏览preview 381ハッカー黒客425フローチャート流程图flowchart 382バックアップ(する)备份backup426ブロードバンド宽带broadband 383パッケージソフト软件包package software427ブロードバンドネットワ宽带网broadband 384バッチ処理批処理batch processing428ブログ博客weblog；385バッチファイル批文件batch file429网絡日誌386パッチ补丁patch430プログラマー程序员programme 387バッテリー电池battery431プログラミング（する）編程programmi 388バッファ緩衝区buffer432プログラミング程序设定计programmi 389バッファオーバーフロー緩衝区溢出buffer overflow433プログラム程序program 390パフォーマンス性能performance434プログラム言語程序語言programmi。
模糊用语
In the news report, the appropriate using does not violate the objective requirements of news language.Accurate expression, but will increase the objectivity and accuracy of language in a certain extent.
why
Some phrases are useful when we want to say that something is similar to something else, but it is not exactly the same. We often use these phrases because we can't find the exact word that we need.
THANK YOU
Fuzzy Language
英语 1141 许江川 29
1
What
When
CONTENT
2
3
How
End
4
What
It to the form of language without shapely defined boundaries and with the characteristics of vagueness.
（知道丈夫有了情人，妻子感觉心情很郁闷，一个人在办公室有气 无力地打着稿子。这时，她的一个好友进来了。）
A: "Let me help you" B: "No, thanks"
FuzzyLogicandFuzzyAlgorithms：模糊逻辑和模糊算法
Fuzzy Logic and FuzzyAlgorithmsCISC871/491Md Anwarul Azim(10036952)Presentation OutlineFuzzy control systemFuzzy Traffic controllerModeling and SimulationHardware DesignConclusion2Figure from Prof. Emil M. Petriu, University of Ottawa6Basic Structure of ControllerDEFUZZIFIER –It extracts a crisp value from a fuzzy set.·Smallest of Maximum.·Largest of Maximum.·Centroid of area.·Bisector of Area·Mean of maximum.FUZZIFIERFuzzifier takes the crisp inputs to a fuzzy controller and converts them into fuzzy inputs.FUZZY RULE BASE (Knowledge base)It consists of fuzzy IF-THEN rules that form the heart of a fuzzy inference system. A fuzzy rule base is comprised of canonical fuzzy IF-THEN rules of the form IF x1 is A1(l) and ... and xn is An (l)THEN y is B(l), where l = 1, 2, ...,M. Should have Completeness, Consistency, Continuity..FUZZY INFERENCE ENGINEFuzzy Inference Engine makes use of fuzzy logic principles to combine the fuzzy IF-THEN rules. Composition based inference (Max/Min,Max/Product)and individual-rule based inference (Mamdani). Other methods like Tsukamoto, Takagi Sugeno Kang (TSK)7“Fuzzy Control”Kevin M. Passino and Stephen Yurkovichhttp://if.kaist.ac.kr/lecture/cs670/textbook/ Fuzzy Traffic controller--Most traffic has fixed cycle controllers that need manual changes to --One of the desirable features of traffic controllers is to dynamically effect the change of signal phase durations--This problem can be solved by use of fuzzy traffic controllers whichadaptively at an intersection.12 /help/toolbox/fuzzy/fp243dup9.html13 /watch?v=hFWGToL-NHw/products/simulink/demos.htmlModeling using Simulink(Cont.)14 /watch?v=hFWGToL-NHw/products/simulink/demos.html15Case Study 2 (Extra)17。
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Some expressions
There
are some fuzzy words used in our communication.
More
or less: There‟s twenty
minutes till the end of the game, more or less.
When
Secondly,
there is no precise language or the speakers do not know how to express by precise language. Such as you describe your new friend , you just say she is a good girl, „good‟ is not a specific word.
Thirdly,
there is no need to use precise language. You are answering the phone, maybe your classmate will ask “who‟s calling?” you do not need to use precise language to describe, maybe you just say one of my friends.
Eg:
In my office job, I have to do the filing, manage the scheduling, answer the phones and all that sort of thing .
I'm
afraid and I regret to say/ I'm sorry to say (a polite expression used when saying 'no')
Its Importance
In
our daily life, communication needs may not be met. Therefore, making the most of fuzzy language is important and beneficial.
Mr.
Tieping says: “We can say in a sense, there is no human natural language without vague language.”
Eg:
When someone asks you where your mother is, but you can not say accurately ,In fact you do not know, but you do not want him to be disappointed ,so you just say my mother is either in the house or at the market.
We
often use these words when both the speaker and the listener know which objects are being discussed, or when we don't know the exact word, or even when there isn't an exact word.
Application of Fuzzy Language in Our communication
模糊语言在我们日常交流 中的应用
Some
people may ask “whether fuzzy language can have a negative influenceபைடு நூலகம்in our daily life?” People always think that language should be clear and accurate in business negotiation. Actually, it can not hinder our normal communication instead helping us.
.
Fuzzy language refers to the form of language without shapely defined boundaries and with the characteristics of vagueness.
Definition
Fuzzy
language, as a constitutive property of natural language, is persuasive in every aspect of language. It is a form of language without shapely defined boundaries and characterized by vagueness.
Four situations
There
are four situations where fuzzy language is preferred.
Firstly
,the speaker occasionally forgets the precise language. Such as your classmate asked how much your new dress was , but you forgot , you could say „not much‟.
As
Liu Fang says, “Vagueness is one of the basic of attributes of natural languages, which is determined by the objective attributes of language and its imperfection in the function of expression.”
Fourthly,
fuzzy language is a better way for speaker to develop a more harmonious social environment and let the communication go as smoothly as possible.
when
you want to say something that may lose listener‟s face ,you often choose fuzzy language which is an instrument for communication .
In
our daily life, fuzzy language is used frequently for different aims and a variety of reasons. Sometimes people are just not willing to make the problems as clear as crystal in order to leave some possibility of retracting the words. Sometimes fuzzy words can make the expressions more economic and get the meaning fully crossed.
there are more examples that you can give, but you don‟t need to, you can use „etcetera‟ „Eg:
We had a great time in Egypt. We saw the Nile, Cairo, the pyramids etcetera.
Conclusion
Fuzziness is one of the essential features of natural languages. Undoubtedly, fuzzy languages enhance the effectiveness of communication, reinforce the flexibility of expression and make our expression more subtle and polite.