2. Based on intuition and judgment No need for a mathematical model Provides a smooth transition between members and nonmembers Relatively simple, fast and adaptive Less sensitive to system fluctuations Can implement design objectives,(difficult to express mathematically), in linguistic or descriptive rules.

3. Applications Domain Fuzzy Logic Fuzzy Control –Neuro-Fuzzy System –Intelligent Control –Hybrid Control Fuzzy Pattern Recognition Fuzzy Modeling

4. Some Interesting Applications Ride smoothness control Camcorder auto-focus and jiggle control Braking systems Copier quality control Rice cooker temperature control High performance drives Air-conditioning systems

5. Conventional or crisp sets are binary. An element either belongs to the set or doesn't. {True, false} {1, 0}

6. Universe (X) Subset A Subset B Subset C Crisp Set/Subset

7. 9 9 9.5 10 e.g. On a scale of one to 10, how good was the dive? Examples of fuzzy measures include close, heavy, light, big, small, smart, fast, slow, hot, cold, tall and short.

8. Fuzzy Indicators Can you distinguish between American and French person?Can you distinguish between American and French person? Some Rules:Some Rules: –If speaks English then American –If speaks French then French –If loves perfume then French –If loves outdoors then American –If good cook then French –If plays baseball then American

9. Fuzzy Indicators Rules may give contradictory indicators {good cook, loves outdoors, speaks French} The right answer is a question of a degree of association Fuzzy logic resolves these conflicting indicators –Membership of the person in the French set is 0.9 –Membership of the person in the American set is 0.1

10. Fuzzy  Probability Probability deals with uncertainty and likelihood Fuzzy logic deals with ambiguity and vagueness

11. Fuzzy  Probability Example #1 –Billy has ten toes. The probability Billy has nine toes is zero. The fuzzy membership of Billy in the set of people with nine toes, however, is nonzero.

12. Example #2 –A bottle of liquid has a probability of ½ of being rat poison and ½ of being pure water. –A second bottle ’ s contents, in the fuzzy set of liquids containing lots of rat poison, is ½. –The meaning of ½ for the two bottles clearly differs significantly and would impact your choice should you be dying of thirst. –50% probability means 50% chance that the water is clean. –50% fuzzy membership means that the water has poison. (cite: Bezdek) #1#1 #2#2

13. Crisp membership functions are either one or zero. e.g. Numbers greater than 10. A ={x | x>10} 1 x 10 A(x)A(x)

14. The set, B, of numbers near to 2 can be represented by a membership function 0 1 2 3 x B(x)B(x)

15. A fuzzy set, A, is said to be a subset of B if e.g. B = far and A=very far. For example...

16. Fuzzy Sets Tall Short

17. Fuzzy Sets Tall Short Tall or Short?

18. Fuzzy Measures 4’4’ 5’5’ 6’6’ 7’7’ Very Tall Tall MediumShort Very Short

19. Membership Function Very TallTallMedium ShortVery Short 4’4’ 5’5’ 6’6’ 7’7’ 1.0

20. Membership Function ShortMedium Tall Very TallTallMedium ShortVery Short 4’4’ 5’5’ 6’6’ 7’7’ 1.0

21. Fuzzy Logic Operations Fuzzy union operation or fuzzy OR

22. Fuzzy Logic Operations Fuzzy intersection operation or fuzzy AND

23. Fuzzy Logic Operations Complement operation

24. Fuzzy Logic Operations Fuzzy union operation or fuzzy OR Fuzzy intersection operation or fuzzy AND Complement operation

25. 0 1 2 3 x AB(x)AB(x) B(x)B(x) A(x)A(x) 1

26. B(x)B(x) A(x)A(x) 1  A+B (x) 0 1 2 3 x

28. Fuzzifier converts a crisp input into a fuzzy variable. Definition of the membership functions must –reflect the designer's knowledge –provide smooth transition between member and nonmembers of a fuzzy set –be simple to calculate Typical shapes of the membership function are Gaussian, trapezoidal and triangular.

29. Assume we want to evaluate the health of a person based on his height and weight. heightThe input variables are the crisp numbers of the person ’ s height and weight. Fuzzification is a process by which the numbers are changed into linguistic words

30. Fuzzification of Height 4’4’ 5’5’ 6’6’ 7’7’ Very ShortShort MediumTallVery Tall VS = very short S = Short M = Medium etc. 1.0

31. Fuzzification of Weight Very HeavyHeavyMedium SlimVery Slim 150lb200lb 250lb300lb 1.0 VS = very slim S = Slim M = Medium etc.

32. Rules reflect expert’s decisions. Rules are tabulated as fuzzy words Rules can be grouped in subsets Rules can be redundant Rules can be adjusted to match desired results

33. Rules are tabulated as fuzzy words –Healthy (H) –Somewhat healthy (SH) –Less Healthy (LH) –Unhealthy (U) Rule function f f  { U, LH, SH, H}

34. f

35. Weight Height Very Slim MediumHeavy Very Heavy Very Short HSHLHUU ShortSHH LHU MediumLHH H U TallUSH H U Very Tall ULHHSHLH

36. For a given person, compute the membership of his/her weight and height Example: –Assume that a person’s height is 6’ 1” –Assume that the person’s weight is 140 lbs

37. Very Short Short MediumTallVery Tall 4’4’ 5’5’ 6’6’ 7’7’ 1.0 0.7 0.3 height ={ VS, S, M, T, VT } height={ 0 0 0.7 0.3 0 }

38. Very SlimSlim MediumHeavyVery Heavy weight ={ VS, S, M, H, VH } Weight={ 0.8 0.2 0 0 0 } 150lb200lb 250lb300lb 1.0 0.8 0.2

39. Weight Height Very Slim MediumHeavy Very Heavy Very Short HSHLHUU ShortSHH LHU MediumHLHU TallHSHU Very Tall ULHHSHLH

40. SHHLHU Very Tall USHH 0.3 ULHH 0.7 ULHSHH Short UULHSHH Very Short Very Heavy Medium 0.20.8 Height Weight SHU HLH

41. Weight Height 0.80.2 Medium (0) Heavy (0) V.Heavy (0) V.Short (0) 00000 Short (0) 00000 0.7 0.2 000 0.3 0.2 000 V.Tall (0) 00000

42. f = {U,LH,SH,H} f = {0.3,0.7,0.2,0.2} Weight Height 0.80.2 V.Short(0)00 Short(0)00 0.7 0.2 0.3 0.2 V.Tall(0)00 Weight Height 0.80.2 V.Short(0)HSH Short(0)SHH 0.7LHH 0.3USH V.Tall(0)ULH

43. f = { U, LH, SH, H} f = { 0.3, 0.7, 0.2, 0.2}

44. Use the fuzzified rules to compute the final decision. Two methods are often used. - Maximum Method(not often used) - Centroid

45. Fuzzy set with the largest membership value is selected. Fuzzy decision: f={U, LH, SH, H} f={0.3, 0.7, 0.2, 0.2} Final Decision(FD)=Less Healthy If two decisions have same membership max, use the average of the two.

47. .....      DD D FD u u     u LH 0.4429 2.02.07.03.0 0.8 2.0 0.6 2.0 0.4 7.0 0.2 0.3     FD Crisp Decision Index (D) = 0.4429

50. Assume that we need to evaluate student applicants based on their GPA and GRE scores. Let us assume that the decision should be Excellent (E), Very Good(VG), Good(G), Fair(F) or Poor(P) An expert will associate the decision to the GPA and GRE score. They are then Tabulated.

51. Assume that we need to evaluate student applicants based on their GPA and GRE scores. For simplicity, let us have three categories for each score [High (H), Medium (M), and Low(L)] Let us assume that the decision should be Excellent (E), Very Good (VG), Good (G), Fair (F) or Poor (P) An expert will associate the decisions to the GPA and GRE score. They are then Tabulated.

53. Excellent = 95-100% Very Good = 90 - 94% Good = 80 - 89% Fair = 70 - 79% Poor = 0 - 69% Issue 94 is VG, but is also very close to Excellent

54. Fuzzifier converts a crisp input into a fuzzy variable. Definition of the membership functions must –Reflects the designer ’ s knowledge –Provides smooth transition between member and nonmembers of a fuzzy set –Simple to calculate Typical shapes of the membership function are Gaussian, trapezoidal and triangular.

55.  GRE = {  L,  M,  H }  GRE

56.  GPA = {  L,  M,  H }  GPA

57. FnFn

58. Assume a student with GRE=900 and GPA=3.6 The decisions on the classification of the applicant are –Excellent –Very good –Etc.

59. GRE=900  GRE = {  L = 0.8,  M = 0.2,  H = 0 }  GRE

60. GPA=3.6  GPA = {  L = 0,  M = 0.4,  H = 0.6 }  GPA

61.  GRE = {  L = 0.8,  M = 0.2,  H = 0 }  GPA = {  L = 0,  M = 0.4,  H = 0.6 }

63. FnFn 0.6 0.4 0.2

64. Converting the output fuzzy variable into a unique number Two defuzzifier methods are often used. –Maximum Method (not often used) –Centroid

65. Fuzzy set with the largest membership value is selected. Fuzzy decision: Fn = {P, F, G,VG, E} Fn = {0.6, 0.4, 0.2, 0.2, 0} Final Decision (FD) = Poor Student If two decisions have same membership max, use the average of the two.

66. FnFn

67. .....       VG E fn FD VGE E i f i f     Final Decision (FD) = Fair Student 70 6.04.02.02.0 606.0704.0802.0902.01000     FD

68. F

69. Feedback (error, change in error) Reference FuzzyController System u Input Output

73. Requires mathematical models Nonlinear processes are linearized Poor Performance when model deviates Difficult to tune for unknown dynamics Poor performance for widely varying operation

74. Poor performance in noisy environments Inclusion of some design objectives can be challenging (e.g. comfort of ride and safety)

75. Engineers are comfortable with the classical control design. –Well-established technologies. –Verifiable overall system stability. –System ’ s reliability can be evaluated.

76. Stability and reliability studies are based on linearized models Most systems do not behave linearly Most systems do drift

77. Neural and Fuzzy Control. Based on intuitions and judgments. Relatively simple, fast and adaptable. Can implement design objectives. Difficult to express mathematically; in linguistic or descriptive rules.

78. No need for mathematical model. Less sensitive to system fluctuations. Design objectives difficult to express mathematically can be incorporated in a fuzzy controller by linguistic rules. Implementation is simple and straight forward.

80. System Inputs Execution Layer …. FLC Supervisor Layer MLFC Outputs

81. -3-20123 LNMNSNZESPMPLP 0 1 0136-3-6 0 1 ZESPMPLPSNMNLN EE  CE