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CLASSICAL RELATIONS AND FUZZY RELATIONS

CLASSICAL RELATIONS AND FUZZY RELATIONS. 報告流程. 卡氏積 (Cartesian Product) 明確關係 (Crisp Relations) Cardinality Operations Properties 合成 (Composition) 模糊關係 (Fuzzy Relations) Cardinality Operations Properties Fuzzy Cartesian Product and Compositon Noninteractive Fuzzy Sets

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CLASSICAL RELATIONS AND FUZZY RELATIONS

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  1. CLASSICAL RELATIONS AND FUZZY RELATIONS

  2. 報告流程 • 卡氏積 (Cartesian Product) • 明確關係 (Crisp Relations) • Cardinality • Operations • Properties • 合成 (Composition) • 模糊關係 (Fuzzy Relations) • Cardinality • Operations • Properties • Fuzzy Cartesian Product and Compositon • Noninteractive Fuzzy Sets • Crisp Tolerance and Equivalence Relations • Fuzzy Tolerance and Equivalence Relations • Value Assignments • Cosine Amplitude • Max-min Method • Other Similarity Methods

  3. Cartesian Product • Producing ordered relationships among sets • X × Y = {(x,y)│x∈X, y∈Y} • All the Ar = A • A1 × A2 × ……. × Ar = Ar

  4. Cartesian Product • Example 3.1 • Set A = { 0,1 } • Set B = { a, b, c } 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)}

  5. Crisp Relations • Measure by characteristic function:χ • X × Y = {(x,y)│x∈X, y∈Y} • Binary relation • χX×Y(x,y)= 1, (x,y) ∈ X × Y 0, (x,y) X × Y • χR(x,y)= 1, (x,y) ∈ X × Y 0, (x,y) X × Y

  6. a b c 1 R = 2 3 Crisp Relations • EX: X={1,2,3} Y={a,b,c} • Relation Matrix • Sagittal diagram

  7. Crisp Relations • Example 3.2 • (一) • X={1,2} Y={a,b} 1 a • Locations of zero 2b • R={(1,a),(2,b)} R X × Y • (二) • A={0,1,2} • UA:universal relation IA:identity relation • 以 A2 為例 • UA = {(0,0),(0,1),(0,2),(1,0),(1,1),(1,2),(2,0),(2,1),(2,2)} IA = {(0,0),(1,1),(2,2)}

  8. Crisp Relations • Example 3.3 • Continous universes • R={(x,y) | y ≥ 2x, x∈X, y∈Y} • χR(x,y)= 1, y ≥ 2x 0, Y < 2x

  9. Cardinality of Crisp Relations • X:n elements Y:m elements n X :the cardinality of X n Y :the cardinality of Y • Cardinality of the relation • n X × Y = nX * nY • power set • The cardinality :P(X × Y) • n P(X × Y) = 2(nXnY)

  10. Operations on Crisp Relations • Union • Intersection • Complement • Containment • Identity (Ø → O and X → E)

  11. Properties of Crisp Relations • 交換律(Commutative law) • 結合律(Associative law) • 分配律(Distributive law) • 乘方(Involution) • 冪等律(Idempotence) • 狄摩根定律(De Morgan’s law) • 排中律(Low of Excluded Middle)

  12. Composition • R={(X1,Y1),(X1,Y3),(X2,Y4)} S={(Y1,Z2),(Y3,Z2)} • Composition oeration • Max-min composition • T=R。S • Max-product comositon • T=R。S

  13. Composition • Example 3.4 • Max-min composition y1 y2 y3 y4 z1 z2 R= x1 S= y1 x2 y2 x3y3 y4 z1 z2 T= x1 x2 x3

  14. Fuzzy Relations • Membership function • Interval [0,1] • Cartesian space X × Y => • Cardinality of Fuzzy Relations • Universe is infinity

  15. Operations on Fuzzy Relations • Union • Intersection • Complement • Containment

  16. Properties of Fuzy Relations • 排中律(Low of Excluded Middle)在Fuzzy 集合中並不成立 !

  17. Fuzzy Cartesian Product • Cartesian product space • Fuzzy relation has membership function • Example 3.5

  18. Fuzzy Composition • Fuzzy max-min composition • Fuzzy max-product composition • 不論 crisp 或 fuzzy 的composition

  19. Fuzzy Composition • Example 3.6 X={x1,x2} Y={y1,y2} Z={z1,z2,z3} • Max-min composition • Max-product compositon

  20. Noninteractive Fuzzy Sets • Fuzzy set on the Cartesian space X =X1 × X2 noninteractive interactive

  21. Noninteractive Fuzzy Sets • Example 3.7

  22. Noninteractive Fuzzy Sets • Example 3.7(續) • Cartesian product

  23. Noninteractive Fuzzy Sets • Example 3.7(續) • Max-min composition • Example 3.8 • Max-min composition

  24. Noninteractive Fuzzy Sets • Example 3.9

  25. Tolerance and Equivalence relations • 自返性(reflexivity) • 對稱性(symmetry) • 傳遞性(transitivity)

  26. Crisp Eqivalence Relation • 自返性(reflexivity) • (xi,xi) R or • 對稱性(symmetry) • (xi,xj) R (xj,xi) R or • 傳遞性(transitivity) • (xi,xj) R and (xj,xk) R (xi,xk) R or

  27. Crisp Tolerance Relation • Also called proximity relation • Only the reflexivity and symmetry • Can be reformed into an equivalence relation • By at most (n-1) compositions with itself

  28. Crisp Tolerance Relation • Example 3.10 • X={x1,x2,x3,x4,x5}={Omaha, Chicago, Rome, London, Detroit} • R1 does not properties of transitivity • e.g. (x1,x2) R1 (x2,x5) R1 but (x1,x5) R1

  29. Crisp Tolerance Relation • Example 3.10(續) • R1 can become an equivalence relation through two compositions

  30. Fuzzy tolerance and equivalence relations • 自返性(reflexivity) • 對稱性(symmetry) • 傳遞性(transitivity)

  31. Fuzzy tolerance and equivalence relations • Equivalence relations • Fuzzy tolerance relation Can be reformed into an equivalence relation • By at most (n-1) compositions with itself

  32. Fuzzy tolerance and equivalence relations • Example 3.11 • It is not transitive • One composition Reflexive and symmetric

  33. Fuzzy tolerance and equivalence relations • Example 3.11(續)

  34. Value assignments • Cartesian product • Closed-from expression • Simple observation of a physical process • No variation • model the process crisp relation • Y= f(X) • Lookup table • Variability exist • Membership values on the interval [0,1] • Develop a fuzzy relation • Linguistic rules of knowledge • If-then rules • Classification • Similarity methods in data manipulation

  35. Cosine Amplitude • X={x1,x2,….,xn} xi={ }

  36. Cosine Amplitude • Example 3.12 • r12=0.836

  37. Cosine Amplitude • Example 3.12(續) • Tolerance relation • Equivalence relation

  38. Max-min Method • rij= where i, j =1,2,…n • Example 3.13 • Reconsider Example 3.12 • Tolerance relation

  39. Summary Q & A

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