The idea of a fuzzy system is introduced in this module.
Its main components are described and discussed in detail using
examples. The results from the fuzzification module drives the
rule base. The fuzzy inference machine is developed which solves
the reasoning. The methods to transform the fuzzy results of the
reasoning process to crisp data is shown in detail. The mainstream
in the fuzzy system of this module is the Mamdani approach which
needs the defuzzification step, but the Takagi-Sugeno-type of
fuzzy system is also introduced which avoids defuzzification.
Module objectives. When you have completed this module you should be able to:
In the previous section, elementary fuzzy terms and fuzzy logic operations have been introduced. In this section, the application to the treatment of rule-based knowledge follows. For this a rule-based fuzzy system is needed, containing a rule base and a reasoning algorithm, which is used to process crisp or fuzzy input values to a crisp or fuzzy output value , see Figure 16.1.