if fuzzy logic isn't exactly true or exactly false, how is it that an exact value can be assigned to it? cos even if the value is between 0 and 1, the value is still an exact value between 0 and 1. or is it? for example if i say 1 is hot and 0 is cold, and the current temperature is 0.8, you'd say it's warm... but isn't warm a value of it's own? i'm trying to understand why fuzzy logic is important and how it makes any difference to anything...
if fuzzy logic is necessary though, does it seek to prove that there's no absolute truth? because I do believe in absolute truth, but then again that would argued with Bayes theorem because that's what I believe... :/ intelligence is confusing.
this is interesting topic , but as u said values btw [(0,1) have a third value which it warm , and this is according to what u define it , u might also define very warm or less warm . i have doubt in what ur saying about absolute truth (what u wxactly mean of it ), since there must be false + truth value ( untill here we call it classic ) but if we defined values btw truth and false then we would have fuzzy .
anyway , fuzzy logic was found to solve real life problem like degrees of temperature or cases of water ( ice, liquid , stream ) .. or maybe in inheritance ( color of flowers )
xD i would like to know if u have mathematics applications xD i only know set indexed work though ...
does this stuff come up in quantum computing
just something I gotta read on in artificial intelligence but it's annoying senseless to me right now... well i'm trying to understand it for now. maybe in future it'll all make sense and i'll do something useful with it, hopefully.
fuzzy logic only improves the degree of resolution of truth values. Without fuzzy its 0 or 1, with fuzzy it could be 0,0.2,0.4,0.6,0.8 or 1 saying "an exact value can NOT be assigned to it" is not accurate.
its like giving more information, than just hot or cold
"if fuzzy logic is necessary though, does it seek to prove that there's no absolute truth?" fuzzy never says, 0 or 1 is not possible, in fuzzy too, 0=cold, 1= hot
to me so far it looks like a nicer way to group all your if statements and conditions
to make the code more robust, an adjustable based on new information
like the fuzzy sets can be easily upgraded based on new observation and data coming in
where as before youd have to do more work to rework the code to set up new condition statements
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