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Overview

Overview. Definitions Teacher Salary Raise Model Teacher Salary Raise Model (revisited) Fuzzy Teacher Salary Model Extension Principle: one to one many to one n-D Carthesian product to y. Teacher Salary Raise Model. Naïve model Base salary raise + Teaching performance

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Overview

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  1. Overview • Definitions • Teacher Salary Raise Model • Teacher Salary Raise Model (revisited) • Fuzzy Teacher Salary Model • Extension Principle: • one to one • many to one • n-D Carthesian product to y

  2. Teacher Salary Raise Model • Naïve model • Base salary raise + Teaching performance • Base + Teaching & research performance (linear) • Base + 80% teaching and 20% research (linear)

  3. TEACHER SALARY RAISE MODEL - Revisited I • More sophistication desired • Flat response in middle • Raise is going to be inflation level in general • We will depart from this only if teaching is • exceptionally good or bad • Ignore research for the time being • if Teaching_Performance < 3, • Raise = 0.01 + 0.04/3(Teaching_Performance); • else if Teaching_Performance < 7, • Raise = 0.05; • else if Teaching_Performance <= 10, • Raise = 0.05/3(Teaching_Performance-7)+0.05;

  4. TEACHER SALARY RAISE MODEL - Revisited I ctd. • 2-D model for both research and teaching • Teach_Ratio = 0.8

  5. Generic MATLAB Code For Salary Raises %Establish constants Teach_Ratio = 0.8 Lo_Raise =0.01;Avg_Raise=0.05;Hi_Raise=0.1; Raise_Range=Hi_Raise-Lo_Raise; Bad_Teach = 0;OK_Teach = 3; Good_Teach = 7; Great_Teach = 10; Teach_Range = Great_Teach-Bad_Teach; Bad_Res = 0; Great_Res = 10; Res_Range = Great_Res-Bad_res; %If teaching is poor or research is poor, raise is low if teaching < OK_Teach raise=((Avg_Raise - Lo_Rasie)/(OK_Teach - Bad_Teach) *teaching + Lo_Raise)*Teach_Ratio + (1 - Teach_ratio)(Raise_Range/Res_Range*research + Lo_Raise); %If teaching is good, raise is good elseif teaching < Good_Teach raise=Avg_raise*Teach_ratio + (1 - Teach_ratio)*(Raise_Range/res_range*research + Lo_Raise); %If teaching or research is excellent, raise is excellent else raise = ((Hi_Raise - Avg_Raise)/(Great_Teach - Good_teach) *(teach - Good_teach + Avg_Raise)*Teach_Ratio + (1 - Teach_Ratio) *(Raise_Range/Res_Range*research+Lo_Raise);

  6. Fuzzy Logic Model For Salary Raises • COMMON SENSE RULES 1. If teaching quality is bad, raise is low. 2. If teaching quality is good, raise is average. 3. If teaching quality is excellent, raise is generous 4. If research level is bad, raise is low 5. If research level is excellent, raise is generous • COMBINE RULES 1. If teaching is poor or research is poor, raise is low 2. If teaching is good, raise is average 3. If teaching or research is excellent, raise is excellent INPUT OUTPUT RULES OUTPUT TERMS INPUT TERMS (assigned) (interpreted)

  7. INPUT OUTPUT RULES OUTPUT TERMS INPUT TERMS (assigned) (interpreted) Fuzzy Logic Model: General Case TEACHING RESEARCH RAISE 1. If teaching is poor or research is poor, raise is low 2. If teaching is good, raise is average 3. If teaching or research is excellent, raise is excellent TEACHING QUALITY RESEARCH QUALITY RAISE (interpreted as good, poor,excellent) (assigned to be: low, average, generous) IF-THEN RULES if x is A the y is B if teaching = good => raise = average BINARY LOGIC FUZZY LOGIC p -->q 0.5 p --> 0.5 q

  8. Definitions • Fuzzy set • Support • Core • Normality • Fuzzy singleton • Cross-over point • Alpha-cut (strong alpha-cut) • Convexity • Fuzzy number • Bandwidth • Fuzzy membership function • Linguistic variable • Set theoretic operations (fuzzy union, • fuzzy intersection, fuzzy complement) • Open-left, open-right & closed fuzzy sets • Symmetry • Cylindrical extension in XxY of a set C(A) • Projection of fuzzy sets • T and S-norm operators • T-co-norm operator

  9. Membership Functions • FUZZY SETS deal with MFs (membership functions) • CLASSICAL (crisp)SET: • FUZZY SET: • FUZZY SETS DESCRIBE VAGUE CONCEPTS • (e.g., fast runner, old man, hot weather, good student) • FUZZY SETS ALLOW PARTIAL MEMBERSHIP • FUZZY LOGICAL OPERATORS • T-NORM OPERATOR for FUZZY Intersection & Union

  10. Fuzzy Set Definition And Notation General Notation: A fuzzy set A in X is defined as a set of ordered pairs and can also be denoted as Fig. 2.1 A = “sensible number of children in a family” B = “about 50 years old X = {0, 1, 2, 3, 4, 5, 6} is the set of # children in a family Fuzzy set A = “sensible number of children in a family” A = {(0,0.1),(1,0.3),(2,0.7),(3,1),(4,0.7),(5,0.3),(6,0.1)} A = 0.1/0+0.3/1+0.7/2+1.0/3+0.7/4+0.3/5+0.1/6 X = R+ is set of possible ages for human beings Fuzzy set B = “about 50 years old”

  11. Membership Functions of Linguistic Variables “Young” “Middle Aged” “Old” Definitions: Core Cross-Over Points and bandwidth Support Fuzzy Singleton Normality alpha-Cut (strong alpha-cut) Fuzzy Numbers Symmetry Figure 2.4 a) Two convex membership functions b) A non-convex membership function

  12. Set-theoretic Operations: Fuzzy Union, Fuzzy Intersection, Fuzzy Complement

  13. Parameterized Membership Functions

  14. Parameterized Mfs - BELL

  15. Mfs of Two Dimensions Cylindrical extension in XxY of a fuzzy set C(A) Projections of a 2-D fuzzy set

  16. Fuzzy Complement The fuzzy complement operator is a continuous function N: [0, 1]  [1, 0] which meets following requirements: N(0) = 1 and N(1) = 0 (boundary) N(a)  N(b) if a <= b (montonicity) Examples:

  17. Fuzzy Intersection or T-Norm The intersection of two fuzzy sets A and B is specified in general by a function T:[0,1]x[0,1]  [0,1] which aggregates the two membership grades as follows: The T-norm operator is a two-place function T(.,.) satisfying T(0,0) = 0; T(a,1) = T(1,a) = a (boundary) T(a,b) <= T(c,d) if a <=c and b <=d (monotonicity) T(a,b) = T(b,a) (cummutativity) T(a,T(b,c)) =T(T(a,b),c) (associativity)

  18. Fuzzy Union or T-conorm (S-norm) The union of two fuzzy sets A and B is specified in general by a function T:[0,1]x[0,1]——>[0,1] which aggregates the two membership grades as follows: The S-norm operator is a two-place function S(.,.) satisfying S(1,1) = 1; S(a,0) = S(0,a) = a (boundary) S(a,b) <=S(c,d) if a<=c and b<=d (monotonicity) S(a,b) = S(b,a) (cummutativity) S(a,S(b,c))=S(S(a,b),c) (associativity)

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