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Classical relations and fuzzy relations
 

Classical relations and fuzzy relations

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    Classical relations and fuzzy relations Classical relations and fuzzy relations Presentation Transcript

    • Classical Relations And Fuzzy Relations
      Baran Kaynak
      1
    • Relations
      This chapter introduce the notion of relation.
      The notion of relation is the basic idea behind numerous operations on sets suchas Cartesian products, composition of relations , difference of relations and intersections of relations and equivalence properties
      In all engineering , science and mathematically based fields, relations is very important
      2
    • Relations
      Similarities can be described with relations.
      In this sense, relations is a very important notion to many different technologies like graph theory, data manipulation.
      Graphtheory
      3
    • Data manipulations
      4
    • Inclassicalrelations (crisprelations),
      Relationshipsbetweenelements of thesetsare
      only in twodegrees; “completelyrelated” and
      “not related”.
      Fuzzyrelationstake on an infinitivenumber of degrees of relationshipsbetweentheextremes of “ completelyrelated” and “ not related”
      5
    • Crisp system
      • Crisp, exact
      - Based on models (i.e.differential equations)
      - Requires complete
      set of data
      - Typically linear
      Fuzzy system
      - Fuzzy, qualitative, vague
      - Uses knowledge (i.e. rules)
      - Requires fuzzy data
      - Nonlinear method
      6
    • Crisp system
      -Complex systems hardto model
      -incomplete information
      leads to inaccuracy
      -numerical
      Fuzzy logic system
      -No traditional modeling,inferences based on knowledge
      - can handle incomplete information to some degree
      -linguistic
      7
    • CartesianProduct
      Example 3.1. The elements in two sets A and B are given as A ={0, 1} and B ={a,b, c}.
      Various Cartesian products of these two sets can bewritten as shown:
      A × B ={(0,a),(0,b),(0,c),(1,a),(1,b),(1,c)}
      B × A ={(a, 0), (a, 1), (b, 0), (b, 1), (c, 0), (c, 1)}
      A × A = A2={(0, 0), (0, 1), (1, 0), (1, 1)}
      B × B = B2={(a, a), (a, b), (a, c), (b, a), (b, b), (b, c), (c, a), (c, b), (c, c)}
      8
    • CrispRelations
      Cartesianproduct is denoted in form A1 x A2 x…..x Ar
      Themostcommoncase is for r=2 andrepresentwith A1 x A2
      The Cartesian product of two universes X and Y is determined as
      X × Y = {(x, y) | x ∈ X,y ∈ Y}
      This form showsthatthere is a matchingbetween X and Y , this is a unconstrainedmatching.
      9
    • CrispRelations
      Every element in universe X is related completely toevery element in universe Y
      Thisrelationship’sstrenght is measuredbythecharacteristicsfunctionχ
      χX×Y(x, y) = 1, (x,y) ∈ X × Y
      0, (x,y) ∉ X × Y
      Completerelationship is showedwith 1 and no relationship is showedwith 0
      10
    • When the universes, or sets, are finite the relation can be conveniently represented by a
      matrix, called a relation matrix.
      X ={1, 2, 3} and Y ={a, b, c}
      Sagittal diagram of an unconstrained relation
      11
    • Specialcases of theconstrainedCartesianproductforsetswhere r=2 arecalledidentityrelationdenoted IA
      IA ={(0, 0), (1, 1), (2, 2)}
      Specialcases of theunconstrainedCartesianproductforsetswhere r=2 arecalleduniversalrelationdenoted UA
      UA ={(0, 0), (0, 1), (0, 2), (1, 0), (1, 1), (1, 2), (2, 0), (2, 1), (2, 2)}
      12
    • Cardinality Of CripsRelations
      TheCardinality of therelation r between X and Y
      is n X x Y = nx * ny
      Power set (P(X x Y)), nP(X×Y) = 2(nXnY)
      13
    • Operations On CripsRelations
      Define R and S as two separate relations on the Cartesian universe X × Y
      Union: R ∪ S -> χR∪S(x, y) : χR∪S(x, y) = max[χR(x, y), χS(x, y)]
      Intersection: R ∩ S -> χR∩S(x, y) : χR∩S(x, y) = min[χR(x, y), χS(x, y)]
      Complement:R ->χR(x, y) : χR(x, y) = 1 − χR(x, y)
      Containment: R ⊂ S ->χR(x, y) : χR(x, y) ≤ χS(x, y)
      14
    • Properties Of CripsRelations
      Commutativity
      Associativity
      Distributivity
      Involution
      Idempotency
      15
      2 × (1 + 3) = (2 × 1) + (2 × 3).
    • Composition
      16
    • CrispBinaryRelation
      17
    • Composition
      Forthesetworelationsletsmake a compositionnamed T
      R = {(x1, y1), (x1, y3), (x2, y4)}
      S = {(y1, z2), (y3, z2)}
      18
    • 19
    • A chain is only as strong as itsweakestlink
      20
    • Example
      Using themax–min composition operation,relationmatrices for Rand S would be expressed as
      µT(x1, z1) = max[min(1, 0), min(0, 0), min(1, 0), min(0, 0)] = 0
      21
    • Example
      Using themax–min composition operation,relationmatrices for Rand S would be expressed as
      µT(x1, z1) = max[min(1, 0), min(0, 0), min(1, 0), min(0, 0)] = 0
      µT(x1, z2) = max[min(1, 1), min(0, 0), min(1, 1), min(0, 0)] = 1
      22
    • FuzzyRelations
      A fuzzy relation R is a mapping from the Cartesianspace X x Y to the interval [0,1], where thestrength of the mapping is expressed by themembership function of the relation μR(x,y)
      μR : A × B -> [0, 1]
      R = {((x, y), μR(x, y))| μR(x, y) ≥ 0 , x ∈ A, y ∈ B}
      23
    • 24
    • Crisp relation vs. Fuzzyrelation
      25
      Crisprelation
      Fuzzyrelation
    • Cardinality of FuzzyRelations
      Since the cardinality of fuzzy sets on any universe is infinity, the cardinality of a fuzzyrelation between two or more universes is also infinity.
      26
    • Operations on FuzzyRelations
      Let R and S be fuzzy relations on the Cartesian space X × Y. Then the following operationsapply for the membership values for various set operations:
      27
      Union: µR∪S(x, y) = max(µR (x, y),µS(x, y))
      Intersection: µR∩S (x, y) = min(µR (x, y),µS (x, y))
      Complement:µR(x, y) = 1 − µR(x, y)
      Containment:R⊂ S ⇒ µR (x, y) ≤ µS (x, y)
    • Fuzzy Cartesian Product and Composition
      A fuzzy relation R is a mapping from the Cartesianspace X x Y to the interval [0,1], where thestrength of the mapping is expressed by themembership function of the relation μR(x,y)
      μR: A × B -> [0, 1]
      R = {((x, y), μR(x, y))| μR(x, y) ≥ 0 , x ∈ A, y ∈ B}
      28
    • Max-minComposition
      Two fuzzy relations R and S are defined on sets A,B and C. That is, R ⊆ A × B, S ⊆ B × C. Thecomposition S•R = SR of two relations R and S isexpressed by the relation from A to C:
      For(x, y) ∈ A × B, (y, z) ∈ B × C,
      µS•R(x, z) = max [min(µR(x, y), µS(y, z))]= ∨ [μR(x, y) ∧ μS(y, z)]
      MS•R= MR•MS(matrixnotation)
      29
    • Max-minComposition
      30
    • Max-productComposition
      Two fuzzy relations R and S are defined on sets A,B and C. That is, R ⊆ A × B, S ⊆ B × C. Thecomposition S•R = SR of two relations R and S isexpressed by the relation from A to C:
      For(x, y) ∈ A × B, (y, z) ∈ B × C,
      μS•R(x, z) = maxy[μR(x, y) • μS(y, z)]
      = ∨y[μR(x, y) • μS(y, z)
      MS•R= MR• MS(matrixnotation)
      31
    • 32
    • Example
      Suppose we have two fuzzy sets, Adefined on a universe of three discretetemperatures, X = {x1, x2, x3}, and Bdefined on a universe of two discrete pressures, Y ={y1, y2}, and we want to find the fuzzy Cartesian product between them. Fuzzy set Acouldrepresent the ‘‘ambient’’ temperature and fuzzy setBthe ‘‘near optimum’’ pressure for a certainheat exchanger, and the Cartesian productmight represent the conditions (temperature–pressurepairs) of the exchanger that are associated with ‘‘efficient’’ operations.
      33
    • FuzzyCartesianproduct, usingµS•R(x, z) = max [min (µR (x, y), µS (y, z))]results in a fuzzyrelation R (of size 3 × 2) representing ‘‘efficient’’ conditions,
      34
    • Example
      X = {x1, x2}, Y = {y1, y2}, and Z = {z1, z2, z3}
      Consider the following fuzzy relations:
      35
      Thentheresultingrelation, T, which relates elements of universe X to elements of universe Z,
      μT(x1, z1) = max[min(0.7, 0.9), min(0.5, 0.1)] = 0.7
    • andbymax–productcomposition,
      36
      μT(x2, z2) = max[(0.8 . 0.6), (0.4 .0.7)] = 0.48
    • Example
      A simple fuzzy system is given, which models the brake behaviour of a car driver depending on the car speed. The inference machine should determine the brake force for a given car speed. The speed is specified by the two linguistic terms"low"and "medium", and the brake force by "moderate"and "strong". The rule base includes the two rules
      (1) IF the car speed is low THEN the brake force is moderate
      (2) IF the car speed is medium THEN the brake force is strong
      37
    • http://virtual.cvut.cz/dynlabmodules/ihtml/dynlabmodules/syscontrol/node123.html
      Short Url: http://goo.gl/T2z5u
      38
    • CrispEquivalenceRelation
      Arelation R on a universeXcan also be thought of as a relation fromXtoX. The relation R is
      an equivalence relation and it has the following three properties:
      Reflexivity
      Symmetry
      Transitivity
      39
    • Reflexivity
      (xi ,xi ) ∈ R or χR(xi ,xi ) = 1
      When a relation is reflexiveevery vertex in the graph originates a single loop, as shown in
      40
    • Symmetry
      (xi, xj ) ∈ R -> (xj, xi) ∈ R
      41
    • Transitivity
      (xi ,xj ) ∈ R and (xj ,xk) ∈ R -> (xi ,xk) ∈ R
      42
    • CrispToleranceRelation
      A tolerance relation R (also called a proximity relation) on a universe X is a relation that
      exhibits only the properties of reflexivity and symmetry.
      A tolerance relation, R, can be
      reformed into an equivalence relation by at most (n − 1) compositions with itself, where n
      is the cardinal number of the set defining R, in this case X
      43
    • Example
      Suppose in an airline transportation system we have a universe composed offive elements: the cities Omaha, Chicago, Rome, London, and Detroit. The airline is studyinglocations of potential hubs in various countries and must consider air mileage between citiesand takeoff and landing policies in the various countries.
      44
    • Example
      These cities can be enumerated as the
      elements of a set, i.e.,
      X ={x1,x2,x3,x4,x5}={Omaha, Chicago, Rome, London, Detroit}
      Suppose we have a tolerance relation, R1, that expresses relationships among these
      cities:
      This relation is reflexive and symmetric.
      45
    • Example
      The graph for this tolerance relation
      If(x1,x5) ∈ R1can become an equivalence relation
      46
    • Example:
      Thismatrix is equivalence relation because it has (x1,x5)
      47
      Five-vertex graph of equivalence relation (reflexive, symmetric, transitive)
    • FUZZY TOLERANCE AND EQUIVALENCE RELATIONS
      Reflexivity μR(xi, xi) = 1
      Symmetry μR(xi, xj ) = μR(xj, xi)
      Transitivity μR(xi, xj ) =λ1 and μR(xj, xk) = λ2 μR(xi, xk) =λwhereλ ≥ min[λ1, λ2].
      48
    • Example
      Suppose, in a biotechnology experiment, five potentially new strains of bacteriahave been detected in the area around an anaerobic corrosion pit on a new aluminum-lithiumalloy used in the fuel tanks of a new experimental aircraft. In order to propose methods toeliminate the biocorrosion caused by these bacteria, the five strains must first be categorized.One way to categorize them is to compare them to one another. In a pairwise comparison, thefollowing " similarity" relation,R1, is developed. For example, the first strain (column 1) hasa strength of similarity to the second strain of 0.8, to the third strain a strength of 0 (i.e., norelation), to the fourth strain a strength of 0.1, and so on. Because the relation is for pairwisesimilarity it will be reflexive and symmetric. Hence,
      49
    • 50
      is reflexive and symmetric. However, it is not transitive
      μR(x1, x2) = 0.8, μR(x2, x5) = 0.9 ≥ 0.8
      but
      μR(x1, x5) = 0.2 ≤ min(0.8, 0.9)
    • 51
      One composition results in the following relation:
      where transitivity still does not result; for example,
      μR2(x1, x2) = 0.8 ≥ 0.5 and μR2(x2, x4) = 0.5
      but
      μR2(x1, x4) = 0.2 ≤ min(0.8, 0.5)
    • 52
      Finally, after one or two more compositions, transitivity results: