engine evaluates the conditional clause ‘if the product temperature is low, AND throughput is low’ by taking the minimum value of the two degree of membership values for temperature being low and flow rate being low. The inference engine further evaluates other clauses in the rule set to obtain fuzzy variables representing the amount of necessary valve change.

 

De-fuzzification: The final step is to transform the result of the fuzzy inference back into a precise control output signal. Li

ke the measurement values, the amount of control action required by each clause can be quantified by fuzzy variables as partly small and partly large, in both directions, by degree of membership values. De-fuzzification combines these values in a way that yields a single output value. This is the change sent to the final actuator.

 

Design an application

Methods for fuzzification, inference, and de-fuzzification are the most confusing aspects of fuzzy logic control, and most treatments of the subject devote most of their content to them. But these details are not key to the performance of a fuzzy control system. Several other fundamental issues have greater impact on the complexity and the performance of a fuzzy application. Inevitably, there is a trade-off between complexity and performance.

 

Types of variables: Fuzzy decision logic is expressed in conditional statements about process variables. This kind of environment does not provide higher order mathematical functions, such as integrals and derivatives, or even simpler ones, such as square root. If the control logic design relies on such functions, they have to be created separately, outside of the fuzzy control environment. For example, if a fuzzy control system needs to respond to the rate of change of a process variable, an incremental change variable must be calculated and added to the set of input variables.

 

Similarly, conditional statements based on the absolute values of measurements may be accu