A fuzzy inference system is a structured way to use fuzzy logic for decision-making, especially when dealing with imprecise or vague information. The workflow of such a system typically involves four main steps: fuzzification, rule evaluation, aggregation, and defuzzification.

The process begins with fuzzification, where crisp input values are transformed into degrees of membership for one or more fuzzy sets. This means that instead of simply classifying an input as "high" or "low," you assign it a value between 0 and 1 representing its degree of belonging to those categories.

Next is rule evaluation. A fuzzy inference system contains a set of if–then rules, such as If temperature is high and humidity is low, then fan speed is high. Each rule evaluates the fuzzified inputs using fuzzy logical operators (like AND or OR) to determine the degree to which the rule applies. This produces a fuzzy output for each rule.

After evaluating all rules, the system moves to aggregation. Here, the outputs of all active rules are combined into a single fuzzy set for each output variable. This step is crucial because multiple rules can fire simultaneously, and their effects must be merged in a meaningful way.

Finally, the system performs defuzzification. In this step, the aggregated fuzzy output is converted back into a single crisp value that can be used for real-world actions or decisions. Common methods for defuzzification include the centroid (center of gravity) and maximum membership principles.