Microsoft Word The use of Fuzzy Logic Control in Manufacturing Systems docx
If (no of products is H) and (Avg. Service time is M) then (No. of machines is H) and (No. of Operators is H) 19
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TheuseofFuzzyLogicControlinManufacturingSystems
18. If (no of products is H) and (Avg. Service time is M) then (No. of machines is H) and (No.
of Operators is H) 19. If (no of products is H) and (Avg. Service time is H) then (No. of machines is H) and (No. of Operators is H) 20. If (no of products is H) and (Avg. Service time is V) then (No. of machines is H) and (No. of Operators is H) 21. If (no of products is V) and (Avg. Service time is VL) then (No. of machines is M) and (No. of Operators is M) 22. If (no of products is V) and (Avg. Service time is L) then (No. of machines is H) and (No. of Operators is H) 23. If (no of products is V) and (Avg. Service time is M) then (No. of machines is H) and (No. of Operators is H) 24. If (no of products is V) and (Avg. Service time is H) then (No. of machines is H) and (No. of Operators is H) 25. If (no of products is V) and (Avg. Service time is V) then (No. of machines is V) and (No. of Operators is V) After plotting the fuzzy relations for the inputs and the outputs, and after writing the rules we can design our controller using mamdani fuzzy inference system. In this project we will use the matlab toolbox for this purpose. Since our goal is just to use the mamdqni fuzzy system, we just give a brief description about how it works. Mamdani Fuzzy inference system consists of fours steps: 1-Fuzzification of the inputs: the input is transformed from numerical value into linguistic term. To do that we can use the membership function for the triangular fuzzy function given before, substituting the right values for a 1 ,a 2 ,a 3 After this step the input will look something like N ’ =.6/L+.3/M+.2/H…etc. 2-Fuzzification of the output: the output is calculated from the inputs in terms of fuzzy linguistics terms 3- Transfer the fuzzy subset of the set of linguistic terms for the output to a fuzzy subset of the set of numerical values. In order to do that we need to use fuzzy composition using the Max-Min Rule. Expressing the max-min Rule as it relates to our specific example, we could write the following.[4] 7 In this design for the first output Y M takes all the real numbers [1,60] The same equation apply for the second output with changing M into P, however the second output Y P takes all the real numbers [1,10] 4- Defuzzification: Transforming the fuzzy set of the output in one numerical value. 𝑀 = ∫ 𝑌 × 𝜇 '" 𝑑𝑌 +, - ∫ 𝜇 '" 𝑑𝑌 +, - Output : I have designed the fuzzy inference system using matlab. In this section I will show some outputs for different input setting: ( ) ( ) ( ) [ ] { } ú ú û ù ê ê ë é Î = Î M M M' M" all for , , min Max Y y y t t y M T t µ µ µ 8 9 The matlab program will allow the user to adjust the two inputs continuously and the output will change corresponding to the change in the inputs. As from the above figures when the input was 200 products and the service time was 10 minutes,; then the number of machines needed was 10 and the number of the operators was 1. From the second output screen when we changed the number of products to 910 and the service time to 20, the output to finish this job was 39 machines and almost 7 operators. Download 110.32 Kb. Do'stlaringiz bilan baham: |
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