NEURO-FUZZY LOGIC

1 X 0 A

age Crisp version for young age

age 1 0 A kid The teenager A young man Crisp definitions of young age

1 age 0 65 Crisp version of old age

1 age Elderly Old Oldest Crisp definitions of old age

FUZZY SETS

age ,9 0,2 0 Possible fuzzy set of young age

age ,2 0 0,8 Possible fuzzy set of old age

MEMBERSHIP FUNCTIONS

65 age 1 0,8 0, Young Old Possible membership functions for young and old ages

IF-THEN LINGUISTIC RULES

age young old middle aged 40 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 lessabout 40 more 32 0,7 0,2 0 minmax level 0 negative zero positive 0,7 0,2 Center of gravity 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 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 mina max0 negative zero positive 0,7 0,2 Center of gravity 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 Fast SmallBig Rule 3 Backward Medium Forwad Medium SmallRule 2 Forwad Medium Forwad Medium Big Rule 1 Right motorLeft motor BackRightFrontLeft 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 Fast SmallBig Rule 3 Backward Medium Forwad Medium Small Rule 2 Forwad Medium Forwad Medium Big Rule 1 Right motorLeft motor BackRightFrontLeft 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 Fast SmallBig Rule 3 Backward Medium Forwad Medium SmallRule 2 Forwad Medium Forwad Medium Big Rule 1 Right motorLeft motor BackRightFrontLeft 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 Membership functions Input Left moto r Left motor Right motor Logical Operations Membership functions Output Distance Left Front Right Back AND Inference Right moto r FUZZY LOGIC CONTROLLER

NEURAL NETWORK CONTROL SYSTEMS

summing unit threshold PERCEPTRON Right motor (weights) Left motor (weights) Threshold S1 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 Right motor Distance SAMPLE OF PERCEPTRON NETWORK FOR CONTROL

ADAPTIVE NEURO-FUZZY CONTROLLER

FLC MLP error desired 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