1. This lecture discusses genetic fuzzy systems and genetic algorithms for tuning different components of fuzzy rule-based systems, including rules, membership functions, and inference parameters. 2. Genetic algorithms can be used for genetic rule learning, genetic rule selection to determine the best rules, and genetic database learning to determine optimal membership function shapes. 3. Simultaneous genetic learning of multiple knowledge base components can improve results by accounting for dependencies between components, though it increases complexity.

1. Introduction to Machine
Learning
Lecture 20
Genetic Fuzzy Systems
Albert Orriols i Puig
http://www.albertorriols.net
htt // lb t i l t
aorriols@salle.url.edu
Artificial Intelligence – Machine Learning
g g
Enginyeria i Arquitectura La Salle
Universitat Ramon Llull

2. Recap of Lecture 19
Slide 2
Artificial Intelligence Machine Learning

3. Today’s Agenda
Continuing with the GFS topics
Genetic tuning
1.
Genetic rule learning
2.
Genetic rule selection
3.
Genetic DB learning
4.
Simultaneous genetic learning of KB components
5.
5
Genetic learning of KB components and inference engine
6.
parameters
Applications
Slide 3
Artificial Intelligence Machine Learning

4. 2. Genetic Rule Learning
How do I get my rules?
g y
The expert may provide me with a set of rules
I may need t learn th
d to l them
Assume Mamdani-type
rules
Slide 4
Artificial Intelligence Machine Learning

5. 2. Genetic Rule Learning
Several models
Pittsburgh-style LCSs
Michigan-style LCSs
Mi hi t l LCS
IRL methods
GCCL
Slide 5
Artificial Intelligence Machine Learning

6. Membership and Rule Tunnig
Slide 6
Artificial Intelligence Machine Learning

7. 3. Genetic Rule Selection
Select the best rules
A bunch of rules is defined
The
Th GA selects the best ones with th aim of
l t th b t ith the i f
Getting the best ones
Getting
G tti a compact rule base
t lb
Slide 7
Artificial Intelligence Machine Learning

8. 3. Genetic Rule Selection
Example of rule selection
p
Slide 8
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9. 4. Genetic DB Learning
Learning the membership function shapes by a GA
g p p y
Do not mix with membership function tuning
Now we are l
N learning th shape
i the h
Slide 9
Artificial Intelligence Machine Learning

10. 5. Simultaneous Learning of KB Components
There is a strong dependency between RB and DB
gp y
Tune them altogether
The
Th search space i
h increases!
!
But, since they are dependant, it may improve the result
Slide 10
Artificial Intelligence Machine Learning

11. 5. Simultaneous Learning of KB Components
Slide 11
Artificial Intelligence Machine Learning

12. 6. Learning of KB and IE Par
Example of learning the rule base and the inference connective
parameters
Slide 12
Artificial Intelligence Machine Learning

13. 6. Learning of KB and IE Par
Slide 13
Artificial Intelligence Machine Learning

14. Applications
Some cool applications among many:
Control of heating and air conditioning systems
1.
Anti-lock break systems
2.
Robot control
3.
3
Slide 14
Artificial Intelligence Machine Learning

15. Control of Heating and AC
The problem
p
Change the speed of a heater fan, based off the room
temperature a d humidity.
e pe a u e and u d y
A temperature control system has four settings
Cold, C l Warm, and H
C ld Cool, W d Hot
Humidity can be defined by:
Low, Medium, and High
Using this we can define the initial rule base
Slide 15
Artificial Intelligence Machine Learning

16. Control of Heating and AC
Initial DB
Slide 16
Artificial Intelligence Machine Learning

17. Control of Heating and AC
Objectives to be minimized
j
Slide 17
Artificial Intelligence Machine Learning

18. Control of Heating and AC
Tuned data base
Slide 18
Artificial Intelligence Machine Learning

19. ABS
Nonlinear and dynamic in nature
y
Inputs for Intel Fuzzy ABS are derived from
Brake
Bk
4 WD
Feedback
Wheel speed
Ignition
Outputs
Pulsewidth
Error lamp
Slide 19
Artificial Intelligence Machine Learning

20. Robot Control
Sensorial inputs
p
Distance to objects
Angles
…
Outputs
O
Speed of wheels
Rotation Pioneer II AT robot
…
Following a mobile object
Following walls
Slide 20
Artificial Intelligence Machine Learning

21. Next Class
Reinforcement Learning and LCSs
Slide 21
Artificial Intelligence Machine Learning

22. Introduction to Machine
Learning
Lecture 20
Genetic Fuzzy Systems
Albert Orriols i Puig
http://www.albertorriols.net
htt // lb t i l t
aorriols@salle.url.edu
Artificial Intelligence – Machine Learning
g g
Enginyeria i Arquitectura La Salle
Universitat Ramon Llull