- A ‘fuzzification interface,’ which transforms precise process measurements into fuzzy input variables;
- An ‘inference engine,’ which can execute logical condition tests using the fuzzy input variables to generate fuzzy output variables; and
- A ‘defuzzification interface,’ which transforms fuzzy output variables into precise control actions.
Fuzzification: In the physic
al world, process variables are measured as precise values with engineering units, such as °F. In the fuzzy world, variables are measured in relative terms, such as high, low, or normal. These terms cover a range of values, and their ranges can overlap. ‘Fuzzification function’ graphic shows a simple example for a temperature measurement of 130°F and a range of 50-150°F.
At the range limits, a numerical measurement is completely ‘low’ or completely ‘high.’ In between it is partly each, and partly ‘normal.’ A value of 130°F, for example, is significantly ‘high.’ Specifically, the conditions take on a range of 0-1.0, so that a value of 130°F is 0.85 ‘high,’ 0.2 ‘normal,’ and 0.075 ‘low.’ These values are the ‘degree of membership’ in each condition, and are the fuzzy values for the input temperature.
The linear or non-linear curves that define the degree of membership value for each condition are called membership functions. They can be expressed mathematically with constants that affect their shape. Changing these constants thereby affects results of the fuzzification function, and is one means of fine-tuning the performance of a fuzzy logic control system.
Inference: An ‘inference engine’ uses these values to evaluate specified logical condition statements in a mathematical way. Logical operators, such as ‘AND,’ ‘OR,’ and ‘NOT’ define how the fuzzy variables are combined to yield a numerical result. For example, a fuzzy inference
