My problem is : how to defuzzyfy in the context of a decisionnal system with
crisp output set ?
2 (simple) examples :
1/ An alarm system for a factory which gives a signal (say a light switching
on for instance) when the work load (WL specified in time units) becomes
"inferior" to the available time (AT) to realize the work.
I consider an alarm level (AL) which is a fuzzy value depending on WL and
I realized a (very) little set of fuzzy rules which modelizes : AL = f (WK,
IF WK is high AND AT is low THEN AL is high
The resulting three dimensionnal curve is rather near the result I aim at.
BUT the question is : for which value of AL do I switch the light on or of ?
With a simple test like : AL < N => switch off and AL > N => switch on, I'm
afraid I loose interest of fuzzy inference.
Is there a graceful way to solve this problem ?
Do I raise the problem in a wrong way ?
2/ Same situation with a mushroom identification system.
Operator inputs a set of characteristics and the system delivers the list
of "best" matching mushrooms.
The ouput of the inference process is a fuzzy set of mushrooms.
In this set, some mushrooms have "high" membership value and other have low.
I'd like to filter the solution set in order to keep the really "best"
Is it the right way to get an alpha-cut of the solution set ?
I'll be insterested by any answer to theses questions or links to any source
I apologize for my bad english.
Thanks a lot.
Hervé POSTEC email@example.com
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