XpertRule Inference from Fuzzy Logic

Inference from a set of fuzzy rules involves fuzzification of the conditions of the rules, then propagating the confidence factors (membership values) of the conditions to the conclusions (outcomes) of the rules. Consider the following rule:

IF (applicant is young) AND (income is low) THEN credit limit is low

Inference from this above rule involves (using fuzzification) looking up the membership value (MV) of the condition 'applicant is young' given the applicant's age, and the MV of 'income is low' given the applicant's salary. The method proposed by Lotfi Zadeh is to take the minimum MV of all the conditions and to assign it to the outcome 'credit limit is low'

In a fuzzy rule base a number of rules with the outcome 'credit limit is low' will be fired. The inference engine will assign the outcome 'credit limit is low', the maximum MV from all the fired rules.

In summary fuzzy inference involves:

Defuzzification of the conditions of each rule and assigning the outcome of each rule the minimum MV of its conditions multiplied by the rule weight.
Assigning each outcome the maximum MV from its fired rules.
Fuzzy inference will result in confidence factors (MV's) assigned to each outcome in the rule base.