Fuzzy Logic - Artificial Intelligence PDF

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SuperiorSard5855

Uploaded by SuperiorSard5855

Pázmány Péter Katolikus Egyetem

PPKE-ITK

Kristóf Karacs

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fuzzy logic artificial intelligence membership functions fuzzy sets

Summary

These lecture notes cover fuzzy logic, a branch of artificial intelligence. The document explains the concept, history, sets, and operations associated with fuzzy logic. It also discusses various types of membership functions and linguistic modifiers.

Full Transcript

Fuzzy logic Artificial intelligence Kristóf Karacs PPKE-ITK 1 History and motivation n Lotfi Zadeh, 1965 n Concept: truth values may apply partially ¨ InBoolean logic truth values are binary ¨ Not able to e...

Fuzzy logic Artificial intelligence Kristóf Karacs PPKE-ITK 1 History and motivation n Lotfi Zadeh, 1965 n Concept: truth values may apply partially ¨ InBoolean logic truth values are binary ¨ Not able to express uncertainty in the occurrence of events, rather their truthfulness n Logical statements are derived from natural language statements 2 1 Fuzzy sets n Sets are identified by linguistic identifiers ¨ “tall”, “young”, “bigger” n Grade of membership , ∈ , 0≤ ≤1 n Fuzzy set A ¨ = , = , , , ,…, , 3 Membership function n Age – “young” Y Y A A Crisp Fuzzy 4 2 Types of membership functions n Piecewise linear ¨ Straight lines (increasing, decreasing) ¨ Triangular 5 Linear curves 6 3 Types of membership functions n Smooth curves ¨ s-curve ¨ z-curve ¨ p-curve 7 Smooth curves 8 4 Operations n Given 9 Fuzzy intersection n 10 5 Operations on fuzzy sets 11 Algebraic properties n Commutativity ¨ a or b = b or a ¨ a and b = b and a n Associativity ¨ (a or b) or c = a or (b or c) ¨ (a and b) and c = a and (b and c) n Distributivity ¨ a or (b and c) = (a or b) and (b or c) ¨ a and (b or c) = (a and b) or (b and c) n DeMorgan rules ¨ ¬(a and b) = (¬a) or (¬ b) ¨ ¬(a or b) = (¬a) and (¬b) 12 6 Algebraic properties n Absorption ¨ (a and b) or a = a ¨ (a or b) and a = a n Idempotency ¨a or a = a ¨ a and a = a n Exclusion (not satisfied) ¨a or ¬a ¹ 1 ¨ a and ¬a ¹ ∅ 13 Linguistic modifiers n Approximation of Fuzzy Sets: scalar → fuzzy set, modifying the "base" of a fuzzy set ¨ about, around, near and close to n Restriction of Fuzzy Sets: modifying the shape ¨ below and above n Intensification and Dilution of Fuzzy Sets ¨ intensification: very (n = 2) and extremely (n = 3) ¨ dilution: somewhat (n = 1/2) and greatly (n = 5/7) 14 7 Linguistic modifiers n Graphical representation 15 8

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