Fuzzy logic was developed by Lotfi Zadeh to mimic human decision-making. It handles intermediate possibilities between YES and NO. Fuzzy logic provides acceptable reasoning when exact answers aren't possible
Fuzzy logic processes multiple truth values through same variable. It generalizes standard logic with partial truth values (0.9, 0.5). First proposed by Lotfi Zadeh in 1965 for information processing
Sugeno model was developed by Takagi, Sugeno, and Kang for generating fuzzy rules. Rules consist of fuzzy sets in antecedent and crisp function in consequent. First-order Sugeno model uses polynomial functions, zero-order uses constant functions
Adaptive control adapts to uncertain parameters in controlled systems. Unlike robust control, it doesn't require prior parameter bounds information. Parameter estimation forms foundation using recursive least squares and gradient descent
Fuzzy logic handles partial truth values between 0 and 1. Introduced by Lotfi Zadeh in 1965 as fuzzy set theory. Based on people's decisions based on imprecise information
Fuzzy logic enables modeling of complex decision problems with incomplete information. Fuzzy sets allow partial membership in sets unlike Boolean logic's strict truth values. Fuzzy decision making considers human subjectivity in problem analysis