neuro-fuzzy logic 1 x 0 a age 1 0 20 crisp version for young age

NEURO-FUZZY LOGIC

1

X

0

A

age

1

0

20

A

Crisp version for young age

2010 16 age

1

0

A kid The teenager

A young man A

Crisp definitions of young age

1

age

0

65

A

Crisp version of old age

1

age

0

65 70 75

Elderly Old

Oldest

A

Crisp definitions of old age

FUZZY SETS

age

1

20 6525 45

0,9

0,2

0

A

Possible fuzzy set of young age

age

1

0

65 25 16 45

0,2

0

0,8

A

Possible fuzzy set of old age

MEMBERSHIP FUNCTIONS

65age

1

0,8

0,2

0

20

4525

Young Old

A

Possible membership functions for young and old ages

IF-THEN

LINGUISTIC RULES

age

1

0

16 65

young old

middle aged

40

A

IF a man have age less than 40 years old, THEN he is a young manIF a man have age more than 40 years old, THEN he is old manIF a man have age 40 years old, THEN he is middle aged man

1

0

negative positive

zero

max min

IF a man is old and his age is more than 40 years old, THEN level of car incidents protection is high (positive)

IF a man is young and his age is less than 40 years old, THEN level of car incidents protection is low (negative)IF a man is middle-aged and his age about 40 years old, THEN level of car incidents protection is normal (zero point).

1

age

less about 40 more

32

0,7

0,2

0

min max level

0

negativezero

positive

0,7

0,2

Center of gravity

A

32 years old age less than 40 with degree 0.7;

Level of car incident protection, in this age, is negative (low) with the same degree

32 years old age is about 40 with degree 0.2;

Level of car incident protection, in this age, is normal (zero) with the same degree

Center of gravity calculation is crisp value of car incident protection level for age 32 years old.

FUZZY LOGIC CONTROL SYSTEMS

distance

1

0

less than 10cm

more than 10 cm

about 10 cm

10 cm

A

IF distance between the robot and the obstacle is less than 10 cm, THEN steer for (a) -10 degr.IF distance between the robot and the obstacle is more than 10 cm, THEN steer for( a )+10degr.IF distance between the robot and the obstacle is 10 cm, THEN go straightforward

1

0

negative positive

zero

a max a min

IF distance between the robot and the obstacle is more than 10 cm, THEN turn to the right ( a is positive)

IF distance between the robot and the obstacle is less than 10 cm, THEN turn to the left ( a is negative)

IF distance between the robot and the obstacle is nearly 10 cm, THEN keep the direction

1

distance

less

nearly 10 cm

more

5 cm

0,7

0,2

0

a min a max0

negative

zero

positive

0,7

0,2

Center of gravity

A

5cm less than 10 cm with degree 0.7;

Steering angle has to be negative with the same degree

(turn to the left)

5 cm is nearly 10 cm with degree 0.2;

Steering angle has to be normal (zero) with the same degree

(keep the direction)

Center of gravity calculation is crisp output of control value

FUZZY LOGIC CONTROLLER

Forward FastForward FastSmallBigBigBigRule 3

Backward Medium

Forwad Medium

SmallRule 2

Forwad Medium

Forwad Medium

BigBigBigBigRule 1

Right motorLeft motorBackRightFrontLeft

Motor SpeedsDistancesRules

IF the distance to the left is Big and the distance in front is Big and the distance to the right is Big and the distance on the back is Big THEN left motor speed is Forward Medium and right motor speed is Forward Medium

Forward FastForward FastSmallBigBigBigRule 3

Backward Medium

Forwad Medium

SmallRule 2

Forwad Medium

Forwad Medium

BigBigBigBigRule 1

Right motorLeft motorBackRightFrontLeft

Motor SpeedsDistancesRules

IF the distance in front is Small (other distances are not considered) THEN left motor speed is Forward Medium and right motor speed is Backward Medium.

Forward FastForward FastSmallBigBigBigRule 3

Backward Medium

Forwad Medium

SmallRule 2

Forwad Medium

Forwad Medium

BigBigBigBigRule 1

Right motorLeft motorBackRightFrontLeft

Motor SpeedsDistancesRules

IF the distance to the left is Big and distance in front is Big and distance to the right is Big and distance to the back is Small THEN left motor speed is Forward Fast and right motor speed is Forward Fast

Small Medium Big

Small Medium Big

Small Medium Big

Small Medium Big

Membership functions

Input

Leftmotor

Left motor

Right motor

Logical Operations

Membership functions

Output

Distance

Left

Front

Right

Back

AND

AND

AND

Inference

Rightmotor

FUZZY LOGIC CONTROLLER

NEURAL NETWORK CONTROL SYSTEMS

summing unit

threshold1x

2x

0w

1w

2w

0x

PERCEPTRON

0.3-0.26 -0.51 -0.77 0.31 0.2 0.2 0.15 0.26Right motor (weights)

0.3 0.2 0.2 0.3 -0.72 -0.46 -0.2 0.26 0.15Left motor (weights)

ThresholdS1 S2 S3 S4 S5 S6 S7 S8 Sensors

SAMPLE OF PERCEPTRON FOR CONTROL

S1

S2

S3

S4

S5

S6

S7

S8

Sensors Summarizing of weights

Motors

Left

Forward

Right

Back

Threshold

Left

motor

Rightmotor

Distance

SAMPLE OF PERCEPTRON NETWORK FOR CONTROL

ADAPTIVE NEURO-FUZZY CONTROLLER

FLC MLP

errordesired performance

Learning parameter performance

actual performance

output

Rule 1: IF (Gradient of Error is Negative Big) AND (Change Gradient of Error is Negative Big) THEN Change of Learning Parameters is Negative Small…………Rule 13: IF (Gradient of Error is Zero Equal) AND (Change Gradient of Error is is Zero Equal) THEN Change of Learning Parameters is Positive Small …………. Rule 25: IF( Gradient of Error is Positive Big ) AND ( Change Gradient of Error is is Positive Big) THEN Change of Learning Parameters is Negative Small

FLC- Fuzzy Logic Controller

MLP- Multilayer Perceptron