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FlouLib

 

This block implements a Mamdani inference with a linguistique representation (or symbolic) of the rulebase.

Input: Membership degree vector

The input vector is generally the result of a symbolic fuzzification but it can also be the output of another block producing a symbolic output (for example another SymbInfMam block), thus allowing the chaining of rules.

Output : Membership degree vector

The block output is a Membership degree vector. The degrees are stored according to the order in the file declaration. The last vector component is a number associated with each block which allows the propagation of some information.

Parameters

It describes the rulebase and is formed as follows::

The line "A-1 B-1 C-2 0.2" corresponds to the rule:

If Input1 is A-1 and Input2 is B-1 the Output  is C-2 weight 0.2

An example of file is as follows:

2 5
A-1 B-1 C-2
A-1 B0 C-1
A-1 B1 C0
A0 B-1 C-1
A0 B0 C0
A0 B1 C1
A1 B-1 C0
A1 B0 C1
A1 B1 C2

·         Projection operator

The projection operator is a T-conorm generalizing the sup.

·         Combinaison operator

The combinaison operator is a T-norm.

·         Premises aggregation operator

The premises aggregation operator is a T-norm.

Remarks

The conventional T-norms and the T-conorms have the following codes:

T-norms

T-conorms

1 -> Min

-1 -> Max

2 -> Product

-2 -> Probabilistic Sum

3 -> Lukasiewicz

-3 -> Lukasiewicz

Other T-norms or T-conorms can be added in the UserOperators.c file of the FlouLib library.

After modification FlouLib must be updated by the instruction: install -Update.