Fuzzy rule-based systems (FRBS) are also known as fuzzy inference systems and fuzzy models. They are based on the fuzzy concept proposed by Zadeh in 1965 . It represents the reasoning of human experts in production rules (a set of fuzzy IF-THEN rules) to handle real-life problems from domains such as control [2, 3, 4], prediction and inference , data mining , bioinformatics data processing , etc.
FRBSs are a competitive alternative to other classic models and algorithms in order to solve classification and regression problems. Generally, an FRBS consists of four functional parts (see Figure 1):
- a fuzzification interface (fuzzifier) which transforms the crisp inputs into degrees of membership function of the fuzzy term of linguistic variable.
- a knowledge base composed of a database and a rulebase. While the database includes the fuzzy set definitions,
- the rulebase contains the fuzzy IF-THEN rules. We will represent the knowledge as a set of rules. Each one has the following structure.
IF premise (antecedent), THEN conclusion (consequent).
- an inference engine which performs the inference operations on the fuzzy IF-THEN rules. There are two kinds of inference for fuzzy systems based on linguistic rules: The Mamdani and the Takagi Sugeno Kang model.
- a defuzzification (defuzzifier) process to obtain the crisp values. There are several methods for defuzzification such as the weighted average, centroid, etc.
Figure 1. The components of FRBS
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