Operator

class floulib.Operator

Bases: object

T_L(x, y)

Lukasiewicz triangular norm.

Parameters:

• x (float) –

• y (float) –

Returns:

max(x + y -1, 0)

Return type:

float

T_P(x, y)

Probabilistic triangular norm.

Parameters:

• x (float) –

• y (float) –

Returns:

x * y

Return type:

float

T_Z(x, y)

Zadeh triangular norm.

Parameters:

• x (float) –

• y (float) –

Returns:

min(x, y)

Return type:

float

S_L(x, y)

Lukasiewicz triangular conorm.

Parameters:

• x (float) –

• y (float) –

Returns:

min(x + y, 1)

Return type:

float

S_P(x, y)

Probabilistic triangular conorm.

Parameters:

• x (float) –

• y (float) –

Returns:

x + y - x * y

Return type:

float

S_Z(x, y)

Zadeh triangular conorm

Parameters:

• x (float) –

• y (float) –

Returns:

max(x, y)

Return type:

float

R_BG(x, y)

Brower-Gödel implication

Parameters:

• x (float) –

• y (float) –

Returns:

1 if x <= y y else

Return type:

float

R_KD(x, y)

Kleene-Dienes implication

Parameters:

• x (float) –

• y (float) –

Returns:

max(1 - x, y)

Return type:

float

R_L(x, y)

Lukasiewicz implication

Parameters:

• x (float) –

• y (float) –

Returns:

min(1 - x + y, 1)

Return type:

float

R_M(x, y)

Mamdani so-called “implication”

This so-called implication, widely used in fuzzy control is in fact a triangular norm (conjonctive representation of the rules).

Parameters:

• x (float) –

• y (float) –

Returns:

min(x, y)

Return type:

float

R_P(x, y)

Larsen so-called “implication”

This so-called implication, also used in fuzzy control is in fact a triangular norm (conjonctive representation of the rules).

Parameters:

• x (float) –

• y (float) –

Returns:

x * y

Return type:

float