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Project Risk Fuzzy Logic document sample
Fuzzy Logic Outline Mamdani fuzzy inference Part 2 The Mamdani fuzzy inference involves four steps: Andrew Kusiak Mamdani fuzzy inference Intelligent Systems Laboratory Sugeno fuzzy inference Fuzzification of the input variables 2139 Seamans Center The University of Iowa Case study Rule evaluation Iowa City, Iowa 52242 - 1527 Summary Aggregation of the rule outputs andrew-kusiak@uiowa.edu Defuzzification http://www.icaen.uiowa.edu/~ankusiak Tel: 319 - 335 5934 Fax: 319 - 335 5669 Based on material provided by Professor Michael Negnevitsky Consider a simple two-input one-output application that two- one- Step 1: Fuzzification Step 2: Rule Evaluation includes three rules: The first step is to take the crisp inputs, x1 and y1 The second step is to take the fuzzified inputs, Rule: 1 Rule: 1 staffing), (project funding and project staffing), and determine μ(x=A1) = 0.5, μ(x=A2) = 0.2, μ(y=B1) = 0.1 and μ(y=B2) = IF x is A3 IF project_funding is adequate the degree to which these inputs belong to each of the OR y is B1 OR project_staffing is small 0.7, and apply them to the antecedents of the fuzzy appropriate fuzzy sets. rules. If a given fuzzy rule has multiple antecedents, THEN z is C1 THEN risk is low Rule: 2 Rule: 2 the fuzzy operator (AND or OR) is used to obtain a IF x is A2 IF project_funding is marginal Project Crisp Input Project Crisp Input single number that represents the result of the funding x1 staffing y1 AND y is B2 AND project_staffing is large Inadequate Marginal Adequate Small Large antecedent evaluation. This number (the truth value) THEN z is C2 THEN risk is normal 1 A1 A2 A3 1 B1 B2 is then applied to the consequent membership 0.7 Rule: 3 Rule: 3 0.5 0.2 0.1 function. 0 0 IF x is A1 IF project_funding is inadequate x1 X y1 Y μ (x = A1) = 0.5 μ (y = B1) = 0.1 THEN z is C3 THEN risk is high μ (x = A2) = 0.2 μ (y = B2) = 0.7 R1: IF project_funding is adequate To evaluate the disjunction of the rule antecedents, OR project_staffing is small THEN risk is low Mamdani’ Mamdani’s rule evaluation Now the result of the antecedent evaluation can be we use the OR fuzzy operation. Typically, fuzzy operation. 1 1 1 applied to the membership function of the expert systems make use of the classical fuzzy A3 B1 0.1 0.1 C1 C2 C3 consequent. 0.0 OR operation union: union: 0 x1 X 0 y1 Y (max) 0 Z The most common method of correlating the rule Rule 1: IF x is A3 (0.0) OR y is B1 (0.1) THEN z is C1 (0.1) consequent with the truth value of the rule μA∪B(x) = max [μA(x), μB(x)] 1 1 0.7 1 antecedent is to cut the consequent membership C1 C2 C3 A2 0.2 B2 0.2 AND function at the level of the antecedent truth. This Similarly, in order to evaluate the conjunction of the 0 0 (min) 0 x1 X y1 Y Z clipping. method is called clipping. Since the top of the rule antecedents, we apply the AND fuzzy operation Rule 2: IF x is A2 (0.2) AND y is B2 (0.7) THEN z is C2 (0.2) membership function is sliced, the clipped fuzzy set intersection: intersection: 1 1 A1 0.5 0.5 C1 C2 C3 loses some information. However, clipping is still μA∩B(x) = min [μA(x), μB(x)] often preferred because it involves less complex and 0 x1 X 0 Z Rule 3: IF x is A1 (0.5) THEN z is C3 (0.5) faster mathematics, and generates an aggregated R2: IF project_funding is marginal R3: IF project_funding is inadequate defuzzify. output surface that is easier to defuzzify. AND project_staffing is large THEN risk is high THEN risk is normal While clipping is a frequently used method, scaling Clipped and scaled membership functions Step 3: Aggregation of the rule outputs offers a better approach for preserving the original Aggregation is the process of unification of the shape of the fuzzy set. The original membership Clipped Scaled outputs of all rules. We take the membership function of the rule consequent is adjusted by Degree of Degree of functions of all rule consequents previously clipped or multiplying all its membership degrees by the truth Membership Membership scaled and combine them into a single fuzzy set. 1.0 1.0 antecedent. value of the rule antecedent. This method, which The input of the aggregation process is the list of generally loses less information, can be useful in C2 C2 functions, clipped or scaled consequent membership functions, fuzzy expert systems. 0.2 0.2 and the output is one fuzzy set for each output variable. 0.0 0.0 Z Z There are several defuzzification methods, but Aggregation of the rule outputs Step 4: Defuzzification probably the most popular one is the centroid The last step in the fuzzy inference process is technique. technique. It finds the point where a vertical line Clipped membership functions defuzzification. defuzzification. Fuzziness helps us to evaluate the would slice the aggregate set into two equal masses. rules, but the final output of a fuzzy system has to be Mathematically this center of gravity (COG) can be 1 1 1 a crisp number. The input for the defuzzification expressed as: C1 C2 C3 0.5 0.5 0.2 process is the aggregate output fuzzy set and the 0.1 0.2 b 0.1 output is a single number. ∫ μ A ( x ) x dx 0 Z 0 Z 0 Z 0 Z z is C 1 (0.1) z is C 2 (0.2) z is C 3 (0.5) ∑ COG = a b ∫ μ A ( x ) dx a Centroid defuzzification method finds a point Center of gravity (COG): Sugeno fuzzy inference representing the centre of gravity of the fuzzy set, A, (0 + 10 + 20 ) × 0.1 + (30 + 40 + 50 + 60 ) × 0.2 + (70 + 80 + 90 + 100 ) × 0.5 COG = = 67 .4 on the interval, ab. ab. 0 . 1 + 0 . 1 + 0 . 1 + 0 .2 + 0 . 2 + 0 . 2 + 0 .2 + 0 . 5 + 0 . 5 + 0 .5 + 0 .5 Mamdani- Mamdani-style inference, requires finding the A reasonable estimate can be obtained by calculating two- centroid of a two-dimensional shape by integrating it over a sample of points. Degree of Membership across a continuously varying function. In general, this process is not computationally efficient. μ( x ) 1.0 1.0 0.8 0.8 0.6 Michio Sugeno suggested to use a single spike, a 0.6 A 0.4 singleton, singleton, as the membership function of the rule 0.4 0.2 singleton, consequent. A singleton, or more precisely a fuzzy 0.0 singleton, singleton, is a fuzzy set with a membership function 0.2 0 10 20 30 40 50 60 70 80 90 100 a b that is unity at a single particular point on the 67.4 Z 0.0 X universe of discourse and zero everywhere else. 150 160 170 180 190 200 210 Sugeno- Sugeno-style fuzzy inference is similar to the The most commonly used zero-order Sugeno fuzzy Sugeno- Sugeno-style rule evaluation zero- Mamdani method. Sugeno has modified a rule model applies fuzzy rules in the following form: 1 1 1 A3 B1 consequent. Instead of a fuzzy set, he used a 0.1 0.1 IF x is A 0.0 OR (max) mathematical function of the input variable. The 0 x1 X 0 y1 Y 0 k1 Z AND y is B Sugeno- format of the Sugeno-style fuzzy rule is Rule 1: IF x is A3 (0.0) OR y is B1 (0.1) THEN z is k1 (0.1) THEN z is k 1 1 1 0.7 IF x is A where k is a constant. A2 0.2 B2 AND 0.2 AND y is B 0 x1 X 0 y1 Y (min) 0 k2 Z THEN y) z is f (x, y) Rule 2: IF x is A2 (0.2) AND y is B2 (0.7) THEN z is k2 (0.2) In this case, the output of each fuzzy rule is constant. where x, y and z are linguistic variables; A and B are All consequent membership functions are represented 1 A1 0.5 1 0.5 fuzzy sets on universe of discourses X and Y, by singleton spikes. 0 x1 X 0 k3 Z y) respectively; and f (x, y) is a mathematical function. Rule 3: IF x is A1 (0.5) THEN z is k3 (0.5) Weighted average (WA): How to make a decision on which method Sugeno- Sugeno-style aggregation of the rule outputs to apply − Mamdani or Sugeno? Sugeno? μ(k1) × k1 + μ(k 2) × k 2 + μ(k 3) × k 3 0.1× 20 + 0.2 × 50 + 0.5 × 80 WA = = = 65 Mamdani method is widely accepted for capturing μ(k1) + μ(k 2) + μ(k 3) 0.1 + 0.2 + 0.5 expert knowledge. It allows us to describe the 1 1 1 1 Sugeno- Sugeno-style defuzzification human- expertise in more intuitive, more human-like 0.5 0.5 0.1 0.2 0.1 0.2 Mamdani- manner. However, Mamdani-type fuzzy inference 0 k1 Z 0 k2 Z 0 k3 Z 0 k1 k2 k3 Z entails a substantial computational effort. z is k1 (0.1) z is k2 (0.2) z is k3 (0.5) ∑ Sugeno method is computationally effective and 0 z1 Z works well with optimization and adaptive Crisp Output techniques, which makes it very attractive in control z1 problems, particularly for dynamic nonlinear systems. Building a fuzzy expert system: A case study Process of developing a fuzzy expert system Step 1: Specify the problem and define 1. Specify the problem and define linguistic variables. linguistic variables A service center keeps spare parts and repairs failed ones. 2. Determine fuzzy sets. There are four main linguistic variables: average A customer brings a failed item and receives a spare waiting time (mean delay) m, repair utilisation of the same type. 3. Elicit and construct fuzzy rules. factor of the service centre ρ, number of servers s, Failed parts are repaired, placed on the shelf, and 4. Encode the fuzzy sets, fuzzy rules and procedures and initial number of spare parts n. thus become spares. to perform fuzzy inference into the expert system. The objective here is to advise a manager of the 5. Evaluate and tune the system. service center on certain decision policies to keep the customers satisfied. Linguistic variables and their ranges Linguistic Value Linguistic Variable: Mean Delay, m Notation Numerical Range (normalised) Step 2: Determine fuzzy sets Fuzzy sets of Mean Delay m Very Short VS [0, 0.3] Short S [0.1, 0.5] Degree of Medium M [0.4, 0.7] Linguistic Variable: Number of Servers, s Fuzzy sets can have a variety of shapes. However, Membership Linguistic Value Small Notation S Numerical Range (normalised) [0, 0.35] a triangle or a trapezoid can often provide an 1.0 Medium Large M L [0.30, 0.70] [0.60, 1] adequate representation of the expert knowledge, 0.8 VS S M Linguistic Variable: Repair Utilisation Factor, ρ Utilisation Factor, and at the same time, significantly simplifies the 0.6 Linguistic Value Notation Numerical Range Low L [0, 0.6] process of computation. 0.4 Medium M [0.4, 0.8] High H [0.6, 1] 0.2 Linguistic Variable: Number of Spares, n Linguistic Value Notation Numerical Range (normalised) 0.0 Very Small VS [0, 0.30] 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Small S [0, 0.40] Mean Delay (normalized) Rather Small RS [0.25, 0.45] Medium M [0.30, 0.70] Rather Large RL [0.55, 0.75] Large L [0.60, 1] Very Large VL [0.70, 1] Fuzzy sets of Number of Servers s Fuzzy sets of Repair Utilisation Factor ρ Fuzzy sets of Number of Spares n Degree of Degree of Degree of Membership Membership Membership 1.0 1.0 1.0 0.8 S M L 0.8 L M H 0.8 VS S RS M RL L VL 0.6 0.6 0.6 0.4 0.4 0.4 0.2 0.2 0.2 0.0 0.0 0.0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Number of Servers (normalized) Repair Utilization Factor Number of Spares (normalized) The Fuzzy Associative Memory Rule Base 1 Step 3: Elicit and construct fuzzy rules (FAM) square 1. If (utilization_factor is L) then (number_of_spares is S) 2. If (utilization_factor is M) then (number_of_spares is M) To accomplish this task, we might ask the expert to No of No of 3. If (utilization_factor is H) then (number_of_spares is L) s spares describe how the problem can be solved using the servers 4. If (mean_delay is VS) and (number_of_servers is S) then (number_of_spares is VL) 5. If (mean_delay is S) and (number_of_servers is S) then (number_of_spares is L) fuzzy linguistic variables defined previously. 6. If (mean_delay is M) and (number_of_servers is S) then (number_of_spares is M) 7. If (mean_delay is VS) and (number_of_servers is M) then (number_of_spares is RL) L M S VS 8. If (mean_delay is S) and (number_of_servers is M) then (number_of_spares is RS) 9. If (mean_delay is M) and (number_of_servers is M) then (number_of_spares is S) Required knowledge also can be collected from 10. If (mean_delay is VS) and (number_of_servers is L) then (number_of_spares is M) M RL RS S 11. If (mean_delay is S) and (number_of_servers is L) then (number_of_spares is S) other sources such as books, computer databases, 12. If (mean_delay is M) and (number_of_servers is L) then (number_of_spares is VS) flow diagrams and observed human behaviour. S VL L M VS S M m Delay 3 + 3x3 rules k dimensional cube for k variables Cube FAM of Rule Base 2 s Rule table 2 Step 4: Encode the fuzzy sets, fuzzy rules s L VS VS VS S VS VS VS S VS L RL M RS Rule m s ρ n Rule m s ρ n Rule m s ρ n and procedures to perform fuzzy VS VS VS M M M S M VS VS VS VS VS VS ρ VL L M ρ 1 VS S L VS 10 VS S M S 19 VS S H VL inference into the expert system VS VS VS S VS VS VS 2 S S L VS 11 S S M VS 20 S S H L S VS VS VS H H M VS S M L s 3 M S L VS 12 M S M VS 21 M S H M VS S M m m L M RS S 4 VS M L VS 13 VS M M RS 22 VS M H M One may choose one of two options: M RS S VS 5 S M L VS 14 S M M S 23 S M H M • Build the system using a programming language S S VS VS 6 M M L VS 15 M M M VS 24 M M H S such as C/C++ or Pascal, or M VS S M s m 7 VS L L S 16 VS L M M 25 VS L H RL • Apply a fuzzy logic development tool such as L S S VS 8 S L L S 17 S L M RS 26 S L H M MATLAB Fuzzy Logic Toolbox or Fuzzy 9 M L L VS 18 M L M S 27 M L H RS M VS VS VS Knowledge Builder. S VS VS VS VS S M L 3x3 = 27 rules The rules constructed from a 3-dimensional cube m (3x3x3 = 27 rules) Three- Three-dimensional plot of Rule Base 1 Three- Three-dimensional plot of Rule Base 1 Step 5: Evaluate and tune the system 0.6 The last, and the most laborious, task is to evaluate 0.6 and tune the system. We want to see whether the number_of_spares 0.5 number_of_spares 0.5 fuzzy system meets the requirements. 0.4 0.4 Several test situations depend on the mean delay, 0.3 0.3 number of servers and repair utilisation factor. 0.2 0.2 0 The Fuzzy Logic Toolbox can generate surfaces 1 0 0.2 0.2 1 system’ useful in analysis of the system’s performance. 0.4 0.6 0.8 0.4 0.6 0.8 0.4 0.4 0.6 0.2 0.6 0.2 mean_delay 0 number_of_servers mean_delay 0 utilization_factor Three- Three-dimensional plot of Rule Base 2 Three- Three-dimensional plot of Rule Base 2 0.35 0.5 However, even now, the expert might not be satisfied with the system performance. number_of_spares 0.3 0.4 number_of_spares 0.25 0.3 To improve the system performance, we may use 0.2 additional sets − Rather Small and Rather Large − 0.2 Servers, on the universe of discourse Number of Servers, 0.15 and then extend the rule base. 0 0 0.2 1 0.2 1 0.8 0.8 0.4 0.6 0.4 0.6 0.4 0.4 0.6 0.2 0.6 0.2 mean_delay 0 number_of_servers mean_delay 0 utilization_factor Cube FAM of Rule Base 3 Three- Three-dimensional plots for Rule Base 3 Modified fuzzy sets of Number of Servers s s VS VS VS L L M RS VS VS VS SL S VS VS VS VS RL RL M RS Degree of VS VS VS RL S S VS 0.35 VS VS VS M M M S Membership VS VS VS 1.0 M VS VS VS VS VS VS s RS VL RL RS VS VS VS 0.3 RS VS VS VS number_of_spares 0.8 VS VS VS ρ L M RS S S VL L M ρ S RS M RL L VS VS VS S VS VS VS H H M VS S M 0.25 RL M RS S 0.6 VS S M L m m M RS S VS 0.4 0.2 s RS S VS VS 0.2 L S S VS S S VS VS 0.15 M 0.0 VS S M RL S S VS m 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 M VS VS VS Number of Servers (normalized) 0.2 1 RS VS VS VS 0.8 0.4 0.6 S VS VS VS 0.4 0.6 0.2 L VS S M 0 mean_delay number_of_servers m Three- Three-dimensional plots for Rule Base 3 Tuning fuzzy systems 4. Review the existing rules, and if required add new 1. Review model input and output variables, and if rules to the rule base. 0.5 required redefine their ranges. 5. Examine the rule base for opportunities to write 0.4 2. Review the fuzzy sets, and if required define hedge rules to capture the pathological behaviour number_of_spares additional sets on the universe of discourse. of the system. 0.3 The use of wide fuzzy sets may cause the fuzzy 6. Adjust the rule execution weights. Most fuzzy 0.2 system to perform roughly. logic tools allow control of the importance of rules 3. Provide sufficient overlap between neighbouring by changing a weight multiplier. 0 0.2 1 triangle- to- sets. It is suggested that triangle-to-triangle and 7. Revise shapes of the fuzzy sets. In most cases, 0.8 0.4 0.6 trapezoid- to- trapezoid-to-triangle fuzzy sets should overlap fuzzy systems are highly tolerant of a shape 0.4 0.6 0 0.2 between 25% to 50% of their bases. approximation. mean_delay utilization_factor