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8 Cards in this Set
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- Back
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Fuzzy Thinking |
1. To describe fuziness 2. The theory of fuzzy sets - A sets that calibrate vagueness 3. Fuzzy logic is based on the idea that all things admit scale of measurement 4. To represent vague and ambigous term 5. Resembles human decision making with its ability to work from approximate data & find a precise solution |
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Uncertainty Term & Certainty Factor |
Certainty Factor (-1 to 1) -1.0 Definitely Not -0.8 Almost Certainly Not -0.6 Probably Not -0.4 Maybe Not -0.2 to +0.2 Unknown +0.4 Maybe +0.6 Probably +0.8 Almost Certainly +1.0 Definitely |
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What is not fuzzy logic |
1. Classical Logic 2. Boolean Logic 3. Both of the above have 2 values - true/false - yes/no
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fuzzy logic vs crisp logic |
Fuzzy Logic 1. For imprecise property 2. Partial membership 3. Fuzzy sets - contain vagueness - continous values
Crisps Logic 1. Precise property 2. Full Membership 3. Crisp set - shows discrete value |
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Fuzzy Logic Pros |
1. Represent vague language naturally 2. Allow flexible engineering 3. Improve model performances 4. Simple to implement |
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Applications in Real Life |
1. Subways in Sendai Japan - to control train accelerations, decelerations, brakings - reduce energy consumption by 10% 2. Consumer Product - Washing Machine - Air Conditioner - Television |
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Fuzzy Rules |
1. Conditional Statements in forms If X is A Then Y is B 2. X & Y are linguistic variable 3. A & B are linguistic values determine by fuzzy sets 4. Fuzzy rules have ranges which make them difference from classical rules |
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Fuzzy Rules Methodology |
1. Set boundaries between 2 values (cold & hot) which will shows the degree of temperature |
