Fuzzy Logic
Discover how fuzzy logic enables AI to handle uncertainty and vagueness in data, unlike traditional binary systems. Learn about fuzzy sets, the fuzzy inference system process, and practical applications such as smart home comfort control. Gain an understanding of how fuzzy logic models human-like reasoning and its limitations, including its use in modern devices like washing machines for adaptive decision-making.
We have witnessed the power of expert systems, but what happens when conditions overlap? For example, in medical diagnosis, symptoms can be vague or overlapping, making it challenging for an expert system to distinguish between similar conditions without precise data. Traditional expert systems, which operate on strict true/false values, fall short in such scenarios. How can we capture "vagueness," something that is neither high nor low but somewhere in between? This is where fuzzy systems come in, handling imprecision and overlaps effectively, which rigid logic systems cannot achieve.
Terminology
Fuzzy logic was introduced by Lotfi Zadeh in 1965 through his seminal paper "Fuzzy Sets." He proposed a mathematical framework for imprecision and vagueness, which traditional binary logic could not handle. Is the weather cold? Some might describe it as "mildly cold," while others might consider it "slightly cold." Fuzzy logic captures these degrees.
Fuzzy sets
Unlike classical sets, where an element belongs to the set or not (binary membership), fuzzy sets allow for varying degrees of membership. A fuzzy set
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