Encoding Rules

IF Taste is Worse AND Quantity is Sleak

THEN Tip is Little

IF Taste is Average AND Quantity is Abundant

THEN Tip is Average

IF Taste is Delicious AND Quantity is Medium

THEN Tip is High

IF Taste is Delicious AND Quantity is Abundant

THEN Tip is High

IFivar1

isMF(x)

ivarnin isMF(x)

THENovar1

isMF(x)

ivarnout isMF(x)

1

IFivar1

isMF(x)

ivarnin isMF(x)

THENovar1

isMF(x)

ivarnout isMF(x)

2

IFivar1

isMF(x)

ivarnin isMF(x)

THENovar1

isMF(x)

ivarnout isMF(x)

3

IFivar1

isMF(x)

ivarnin isMF(x)

THENovar1

isMF(x)

ivarnout isMF(x)

4

IFivar1

isMF(x)

ivarnin isMF(x)

THENovar1

isMF(x)

ivarnout isMF(x)

k

Encoding – Membership functions

mean1

2

3

4

numberMFs

Genetic MFEncoding

Inference MFEncoding

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mean

mean

mean

R

R

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LR

L

L

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mean

(x)

1.0

0.5

-3R -2R-1R 1L 2L 3L0

Objective Function (OF)

• Objective function is the MSE (mean squared error) on supervisory data of the given Fuzzy System individual

Genetic Operators

• Applied on genetic MF encoding of membership functions

• Types– Crossover– Mutation– Set similar– Set zero– Set unity

Genetic Operators – Mutation (random)

parent

LRmean

mutation cell

LRmean

child

Genetic Operators - Crossover

cutoff

{

child 1 child 2

parent Bparent A

LRmean LRmean

Neuro Fuzzy

Input 1

Input 2

InputLayer

HiddenLayer

OutputLayer

Output 1

Rule Implication

Defuzzification

Rule

W1,1

MFComposition

W1,2

W1,3