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Fuzzy logic is all about the relative importance of precision: How important is
it to be exactly right when a rough answer will do? All books on fuzzy logic
begin with a few good quotes on this very topic, and this is no exception. Here
is what some clever people have said in the past.
•Fuzzy logic is tolerant of imprecise data.
Everything is imprecise if you look closely enough, but more than that, most
things are imprecise even on careful inspection. Fuzzy reasoning builds this
understanding into the process rather than tacking it onto the end.
•Fuzzy logic can model nonlinear functions of arbitrary complexity.
You can create a fuzzy system to match any set of input-output data. This
process is made particularly easy by adaptive techniques like ANFIS
(Adaptive Neuro-Fuzzy Inference Systems), which are available in the Fuzzy
Logic Toolbox.
•Fuzzy logic can be built on top of the experience of experts.
In direct contrast to neural networks, which take training data and generate
opaque, impenetrable models, fuzzy logic lets you rely on the experience of
people who already understand your system.
•Fuzzy logic can be blended with conventional control techniques.
Fuzzy systems don’t necessarily replace conventional control methods. In
many cases fuzzy systems augment them and simplify their implementation.
•Fuzzy logic is based on natural language.
The basis for fuzzy logic is the basis for human communication. This
observation underpins many of the other statements about fuzzy logic.
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