Lecture 4: Fuzzy Inference System

General definition: A computing framework based on the concepts of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning.

Interpretation: Specifically, it is a framework used to model and infer the behaviour of a system. There two important parts:

  1. Encoding knowledge as a set of if-then rules
  2. Predict the system behaviour using the encoded knowledge

The basic structure of FIS are as follows:

  1. Inputs
    • Can be fuzzy or crisp
  2. Outputs
    • Always almost fuzzy set
  3. Rule Base
    • Number of fuzzy if-then rules, each describing local behavior mapping
  4. Reasoning Mechanism

Note on rule base

If a antecedent has two different consequences, then the rule base is considered to be inconsistent.

Reasoning Mechanism: Defuzzification

Extracting a crisp value from a fuzzy set. The method of interest for the course are as follow:

Centroid of Area (COA) or Centre of Gravity (COG)

$$Z_{COG} = \frac{\int_z \mu_A(z)zdz}{\int_z \mu_A(z)dz}$$ Where $z$ is represents the position on the bar and $\mu_A(z)$ represents the density.