Antecedent-consequent linguistic rules

The fuzzy rulebase consists of a set of antecedent-consequent linguistic rules of the form... 

The antecedent and consequent of a rule consist of two parts an object and its value, which are linked by an operator. The operator identifies an object and assigns a value. Operators, such as is, are, is not and are not, are usually used to assign a symbolic value to a linguistic term. Mathematical operators can also be used to define an object as numerical and assign it a numerical value. For example,... 

To get a feel of the fuzzy definition set, detection likelihood, as shown in Fig. lV/2.2.3-1, has been transformed into a fuzzy definition. A typical fuzzy membership is shown in Fig. lV/2.6.4-2. Actual fuzzy values are derived based on the fuzzy rule set. Fuzzy inputs are evaluated using a rule-based set, so that criticality and RPN calculations can be made. In the fuzzification process, with help of crisp ranking, set S O D is converted into fuzzy representation so that these can be matched with the rule base. Here, the if then rule has two parts an antecedent (which is compared to input) and consequence (which is the result). On the other hand, in the defuzzification process, the reverse takes place. It is possible to automate FMEA using fuzzy logic and rule-based systems. The rule allows quantitative data such as occurrence to be easily combined with judgmental and quantitative data (such as severity and detectability) very easily and uniformly. The rule based on the linguistic variables is more expressive and useful (for further reading see Ref. [11]). 

The rules relating the input and the output variables are written in an "if...then" linguistic format such as "if temperature is cold and discharge rate is high then SOC is low." The "if "terms are referred to as antecedents and the term following the "then" is referred to as the consequent. 

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