Introduction to MachineIntroduction to Machine LearningLearning

Lecture 20Lecture 20Genetic Fuzzy Systems

Albert Orriols i Puightt // lb t i l thttp://www.albertorriols.net

aorriols@salle.url.edu

Artificial Intelligence – Machine Learningg gEnginyeria i Arquitectura La Salle

Universitat Ramon Llull

Recap of Lecture 19

Slide 2Artificial Intelligence Machine Learning

Today’s Agenda

Continuing with the GFS topicsContinuing with the GFS topics1. Genetic tuning

2. Genetic rule learning

3. Genetic rule selection

4. Genetic DB learning

5 Simultaneous genetic learning of KB components5. Simultaneous genetic learning of KB components

6. Genetic learning of KB components and inference engine parametersparameters

Applications

Slide 3Artificial Intelligence Machine Learning

2. Genetic Rule LearningHow do I get my rules?g y

The expert may provide me with a set of rules

I d t l thI may need to learn them

Assume Mamdani-type rules

Slide 4Artificial Intelligence Machine Learning

2. Genetic Rule LearningSeveral models

Pittsburgh-style LCSs

Mi hi t l LCSMichigan-style LCSs

IRL methods

GCCL

Slide 5Artificial Intelligence Machine Learning

Membership and Rule Tunnig

Slide 6Artificial Intelligence Machine Learning

3. Genetic Rule SelectionSelect the best rules

A bunch of rules is defined

Th GA l t th b t ith th i fThe GA selects the best ones with the aim ofGetting the best onesG tti t l bGetting a compact rule base

Slide 7Artificial Intelligence Machine Learning

3. Genetic Rule SelectionExample of rule selectionp

Slide 8Artificial Intelligence Machine Learning

4. Genetic DB LearningLearning the membership function shapes by a GAg p p y

Do not mix with membership function tuning

N l i th hNow we are learning the shape

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5. Simultaneous Learning of KB Components

There is a strong dependency between RB and DBg p yTune them altogether

Th h i !The search space increases!

But, since they are dependant, it may improve the result

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5. Simultaneous Learning of KB Components

Slide 11Artificial Intelligence Machine Learning

6. Learning of KB and IE Par

Example of learning the rule base and the inference connective parameters

Slide 12Artificial Intelligence Machine Learning

6. Learning of KB and IE Par

Slide 13Artificial Intelligence Machine Learning

Applications

Some cool applications among many:1. Control of heating and air conditioning systems

2. Anti-lock break systems

3 Robot control3. Robot control

Slide 14Artificial Intelligence Machine Learning

Control of Heating and AC The problemp

Change the speed of a heater fan, based off the room temperature and humidity.e pe a u e a d u d y

A temperature control system has four settingsC ld C l W d HCold, Cool, Warm, and Hot

Humidity can be defined by:Low, Medium, and High

Using this we can define the initial rule base

Slide 15

Using this we can define the initial rule base

Artificial Intelligence Machine Learning

Control of Heating and AC Initial DB

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Control of Heating and AC Objectives to be minimizedj

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Control of Heating and AC Tuned data base

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ABSNonlinear and dynamic in naturey

Inputs for Intel Fuzzy ABS are derived fromB kBrake

4 WD

Feedback

Wheel speedWheel speed

Ignition

Outputs Pulsewidth

Error lamp

Slide 19Artificial Intelligence Machine Learning

Robot ControlSensorial inputsp

Distance to objects

AnglesAngles

…

OOutputsSpeed of wheels

Rotation

…Pioneer II AT robot

Slide 20Artificial Intelligence Machine Learning

Following walls Following a mobile object

Next Class

Reinforcement Learning and LCSs

Slide 21Artificial Intelligence Machine Learning

Introduction to MachineIntroduction to Machine LearningLearning

Lecture 20Lecture 20Genetic Fuzzy Systems

Albert Orriols i Puightt // lb t i l thttp://www.albertorriols.net

aorriols@salle.url.edu

Artificial Intelligence – Machine Learningg gEnginyeria i Arquitectura La Salle

Universitat Ramon Llull