Knowledge Base of fuzzy system

Knowledge Base:
The KB establishes the fundamental part of the Mamdani FRBS. It serves as the repository of the problem specific knowledge —that models the relationship between input and output of the underlying system.
Notice that the KB contains two different information levels

1. A Database (DB):
Containing :
• The linguistic term sets (the linguistic rules ).
• The membership functions.
• The scaling factors or scaling functions.
that are used to transform between the universe of discourse in which the fuzzy sets are defined to the domain of the system input and output variables.
The types of salling are:

a) Linear scalling:
Scaling allows it to define the fuzzy membership functions over a normalised universe of discourse. This variable is normalised by applying the parameterised scaling function, which incorporates the context information through the parameters min and max(0 and 1), whether fixed, tuned or learned scaling functions are employed.
b) Non-linear scaling:
This scalling provides a remedy to this problem as it modifies the relative distribution and changes the shape of the membership functions.
2. Rule Base(RB):
A rule base (RB) is comprised by a collection of linguistic rules that
are joined by the also operator. In other words, multiple rules can fire simultaneously for the same input.
The RB can present several structures.