os of fuzzy logic is in the extrapolation of data from seta of variables. A fairly apt example of this is the variable lamp. Conventional boolean logical processes deal well with the binary nature of lights. They are either on, or off. But introduce the variable lamp, which can range in intensity from logically on to logically off, and this is where applications demanding the application of fuzzy logic come in. Using fuzzy algorithms on sets of data, such as differing intensities of illumination over time, we can infer a comfortable lighting level based upon an analysis of the data. Taking fuzzy logic one step further, we can incorporate them into fuzzy expert systems. This systems takes collections of data in fuzzy rule format. According to Dr. Lotfi, the rules in a fuzzy logic expert system will usually follow the following simple rule: "if x is low and y is high, then z is medium". Under this rule, x is the low value of a set of data (the light is off) and y is the high value of the same set of data (the light is fully on). z is the output of the inference based upon the degree of fuzzy logic application desired. It is logical to determine that based upon the inputs, more than one output (z) may be ascertained. The rules in a fuzzy logic expert system is described as the rulebase. The fuzzy logic inference process follows three firm steps and sometimes an optional fourth. They are: 1. Fuzzification is the process by which the membership functions determined for the input variables are applied to their true values so that truthfulness of rules may be established. 2. Under inference, truth values for each rule's premise are calculated ...
