Defuzzification process of fuzzy sets in RDBMS?

Okay, first of all, are you using a probabilistic logic or a fuzzy logic. While similar, they're not identical. If you're really modeling probabilities here, then you need to look into this via Bayes Theorem as a conditional probability.

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I have a table that stores all values..eg x1, x2, x3 which determines fuzzy sets. Degree of membership is assigned to each using mathematical functions for Low, Med, High. My rule 1 states that if x1 is high AND x2 is medium then probability of output is z.

I then take min{x1,x2} to evaluate the rule. Rule 2 states that if x2 is high OR x3 is medium then output is max{x2,x3}. Now to defuzzify I take aggregation of rule consequences to find out the output of the 2 rules.

I have all degrees of membership defined (0 to 1) for x1 x2 x3 for each rule. How do I defuzzify? Artificial-intelligence rdbms paradox fuzzy-logic link|improve this question edited Mar 4 '09 at 15:25 asked Mar 4 '09 at 1:21CGF91211 90% accept rate.

Okay, first of all, are you using a probabilistic logic or a fuzzy logic. While similar, they're not identical. If you're really modeling probabilities here, then you need to look into this via Bayes Theorem as a conditional probability.

If these are really fuzzy truth values, then you need to have a model of set-membership, which we need to know.

The sets are fuzzy and Bayesian reasoning won't be applied.. Fuzzy output is defined as the degree of membership to which they belongnot the probability as output. I forgot that the consequent of the rule could have multiple parts. This was the missing link!

Now ok! The problem is now solved - – CGF Mar 4 '09 at 15:22 Yay! Haven't done fuzzy systems in a long time, fun to think back to them.

– Charlie Martin Mar 4 '09 at 15:28.

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