# 04 Sistem Fuzzy.ppt

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Chapter 4: Fuzzy Inference Systems   Introduction (4.1)  Mamdani Fuzzy models (4.2)  Sugeno Fuzzy Models (4.3)  Tsukamoto Fuzzy models (4.4)  Other Considerations (4.5)  –  Fuzzy modeling  2 Introduction (4.1)  Fuzzy inference is a computer paradigm based on fuzzy set theory, fuzzy if-then- rules and fuzzy reasoning  Applications:data classification , decision analysis, expert systems, times series predictions, robotics & pattern recognition  Different names; fuzzy rule-based system, fuzzy model, fuzzy associative memory, fuzzy logic controller & fuzzy system Fuzzy System: (Rule Base, Data Base, Reasoning Mechanism)  3 Introduction (4.1)   Structure  – Rule base   selects the set of fuzzy rules  – Database (or dictionary)   defines the membership functions used in the fuzzy rules  – A reasoning mechanism   performs the inference procedure (derive a conclusion from facts & rules!)  Defuzzification: extraction of a crisp value that best represents a fuzzy set  – Need: it is necessary to have a crisp output in some situations where an inference system is used as a controller   4 Block diagram for a fuzzy inference system is A 1 x  x  y is B 1 y is B 2 y is B r  is A r  x  x  x  is A 2   aggregator defuzzifier  y Rule 1Rule 2Rule r Fuzzy w  1 w   2 w  r

Oct 7, 2019

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Oct 7, 2019
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