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 [1]. 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 [5], data mining [6], bioinformatics data processing [7], 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

 

References

[1] L.A. Zadeh, "Fuzzy sets", Information and Control, vol. 8, pp. 338 – 353, 1965.

[2] C.C. Lee, "Fuzzy Logic in control systems: Fuzzy Logic controller part I", IEEE Trans. Syst., Man, Cybern., vol. 20, no.2, pp. 404-418, 1990.

[3] C.C. Lee, "Fuzzy Logic in control systems: Fuzzy Logic controller part II", IEEE Trans. Syst., Man, Cybern., vol. 20, no.2, pp. 419-435, 1990.

[4] W. Pedrycz, "Fuzzy Control and Fuzzy Systems," New York: Wiley, 1989.

[5] Pei-Chann Chang, Chen-Hao Liu, “A TSK type fuzzy rule based system for stock price prediction, Expert Systems with Applications,” Volume 34, Issue 1, pp 135-144, ISSN 0957-4174, 2008.

[6] Li-Xin Wang, “The WM method completed: a flexible fuzzy system approach to data mining,” IEEE Transaction on Fuzzy Systems, Vol 11 (6), pp. 768 – 782, 2003.

[7] Bill C.H. Chang and Saman K. Halgamuge, “Protein motif extraction with neuro-fuzzy optimization,” Bioinformatics 18 (8): pp. 1084-1090, 2002.