KNOWLEDGE BASED SYSTEMS(Sistem Berbasis Pengetahuan)

(lecture note Jan 2009)

Ir. Wahidin Wahab M.Sc Ph.DDept.Teknik Elektro

Universitas IndonesiaEmail: wahidin.wahab@ ui.ac.id

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Bahan Kuliah

• Fuzzy Logic Theory • Neural Network• Genetic Algorithm• Applications

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References• Yen, J. & Langari, R., ‘FUZZY LOGIC,

INTELLIGENCE, CONTROL AND INFORMATION’, Prentice Hall Inc. 1999

• Ross, Timothy, ‘FUZZY LOGIC ENGINEERING’, Prentice Hall Inc. 1995

• Altrock, C.V., Fuzzy Logic and NeuroFuzzy Applications explained. Prentice Hall, N.J. 1992

• Lefteri H. Tsoukalas and Robert Uhrig, FUZZY AND NEURAL APPROACHES IN ENGINEERING, John Wiley & Sons Inc. S’pore 1997

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FUZZY LOGIC

• First introduced in 1965 by Dr. Lofti Zadeh• Published as a seminal work “Fuzzy Sets”• In the Journal Information and Control• Since then it has been the focus of many

Research investigation by– Mathematicians– Scientists– Engineers…….. Around the world.

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Industry• Popular Consumer Products• Japan has over 1000 Patents in Fuzzy

Technology and have already earned billions of US$ in the sales of Fuzzy-logic based products

• The word “Fuzzy” is the keyword fo 1990s in Japan.

• Frost&Sullivan projected 20% annual growth rate for Fuzzy product market

• Would be one of the world 10 hottest technology in 21st Century.

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Future Impact• In 1990 & 1991 NTIS* Studies show that

Fuzzy Logic will have significant Future Impact.

• The Integration of Fuzzy Logic with Neural Network and Genetic Algorithm is now making Automated Cognitive System a reality in Many discipline.

• Cognitive System, is one that can learn and reason.

*(NTIS= National Technical Information Service)

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What is “FUZZY” ?• Webster’s Dictionary :

Fuzzy 1:covered with fuzz or resembling fuzz2: not clear, indistinct,3: Blurred

• In Technical sense denotes mathematical or engineering disciplines based on the theory of fuzzy sets and fuzzy logic. fuzzy control, fuzzy modelling, fuzzy decision making.

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Why Fuzzy Sets?Why Fuzzy Sets?

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Fuzzy Sets and Fuzzy Logic

• A relatively new methods for representing – uncertainty and – reasoning under uncertainty.

• Type of uncertainty :– Chances, randomness(stochastic)– Imprecision, vagueness, ambiguity (non-

stochastik)

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Fuzzy Sets and Fuzzy Logic

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Conventional (Boolean) Set Theory:Conventional (Boolean) Set Theory:

Slide 11

“Strong Fever”

40.1°C40.1°C

42°C42°C

41.4°C41.4°C

39.3°C39.3°C

38.738.7°°CC

37.237.2°°CC

3838°°CC

Fuzzy Set Theory:Fuzzy Set Theory:

40.1°C40.1°C

42°C42°C

41.4°C41.4°C

39.3°C39.3°C

38.7°C

37.2°C

38°C

““MoreMore--oror--LessLess”” Rather Than Rather Than ““EitherEither--OrOr”” !!

“Strong Fever”

Fuzzy Set Theory

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Clasical SClasical Set of natural numbers et of natural numbers smaller than 5smaller than 5

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Classical Set ApproachClassical Set Approach

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Logic PropositionsLogic Propositions

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Fuzzy Set ApproachFuzzy Set Approach

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Fuzzy Logic PropositionsFuzzy Logic Propositions

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Subjective and Context DependentSubjective and Context Dependent

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Fuzzy Logic : Basic Concept• Fuzzy Sets :

– Sets with smooth boundaries• Linguistic Variables :

– Variables whose values are both qualitatively and quantitatively described by a Fuzzy Set

– Enables its values to be described both • Qualitatively by a linguistic term• Quantitatively by a membership function

• Possibility Distributions:– A degree of possibility that put Constrains on the

value of a linguistic variable imposed by assigning it a Fuzzy Set

• Fuzzy If Then Rules :– A knowledge representation scheme for describing a

functional mapping that generalized an implication.

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• A Linguistic Variable enables its value to be described both – Qualitatively by a linguistic term

(a symbol serving the name of the fuzzy set)– And Quantitatively by a corresponding

membership function( Which expresses the meaning of the fuzzy set)

• The linguistic term is used to express concepts and knowledge in human communications

• The membership function is usefull for processing numeric data. 19

Linguistic Variables

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Linguistic Variables• A linguistic variable is like a composition of a

symbolic variable and a numeric variable.Eg. Shape = “cylinder”

Height = 4 cm• Numeric variables are frequently used in

science, engineering, mathematics, medicine, and many others disciplines

• Symbolic variables play an important role in artificial intelligence and decision sciences.

• The modifiers such as : ‘very heavy’, ‘light’, ‘moderate’ hedges

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...Terms, Degree of Membership, Membership Function, Base Variable......Terms, Degree of Membership, Membership Function, Base Variab...Terms, Degree of Membership, Membership Function, Base Variable...le...

Linguistic VariableLinguistic Variable

Slide 21

39°C 40°C 41°C 42°C38°C37°C36°C

1

0

µ(x)low temp normal raised temperature strong fever

… pretty much raised …… pretty much raised …

... but just slightly strong …... but just slightly strong …

A Linguistic Variable A Linguistic Variable Defines a Concept of Our Defines a Concept of Our Everyday Language!Everyday Language!

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Possibility Distributions

Eg. Crips Set : Age(suspect) = [20, 30]

Fuzzy set : µYoung(x)

The possibility that the suspect is 19 years old is 0.7The possibility that the suspect is 21 -28 years oled is 1, etc…

Supposed the Police reported that the age of the bombing terrrorist suspect Is between 20 and 30 years oled, then we can define :

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Fuzzy Rules• Implementation of Fuzzy Set using Fuzzy

If Then Rules.• is the basic unit for capturing knowledge.• Has been successfully applied to:

– Control systems, decision making, pattern recognition and system modelling.

– In industrial applications such as: consumer product, robotics, manufacturing, process control, medical imaging, to financial trading.

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Fuzzy Rules

Can be viewed from several viewpoints:• Conceptually : it can be understood

using metaphor of drawing a conclusion using a panel of expert.

• Mathematically: it can be viewed as an interpolation scheme.

• Formally: it is a generalization of a logic inference called Modus Ponens.

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Structure of Fuzzy Rules

• A Fuzzy rule has 2(two) components– an IF part as the antecedent– a THEN part as the consequent.

IF <antecedent> THEN <consequent>

• The antecedent describes a condition• The consequent describe a conclusion

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Rules for Automatic Washing Machine

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ApplicationsApplications

Pattern recognition and classificationFuzzy clusteringImage and speech processingFuzzy controlFuzzy systems for predictionMonitoringDiagnosisQualitative modelingQualitative dynamic fuzzy simulationSystem identificationOptimization and decision making

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And more recently FUZZY Machines have been developed

FFUZZYUZZY LLOGICOGIC & F& FUZZYUZZY SSYSTEMS YSTEMS UNCERTAINITY AND ITS TREATMENT

Advertisement

Extraklasse Washing Machine - 1200 rpmThe Extraklasse machine has a number of features which will make life easier for you.

Fuzzy Logic detects the type and amount of laundry in the drum and allows only as much waterto enter the machine as is really needed for the loaded amount. And less water will heat up quicker - which means less energy consumption.Foam detectionToo much foam is compensated by an additional rinse cycle: If Fuzzy Logic detects the formation of too much foam in the rinsing spin cycle, it simply activates an additional rinse cycle. Fantastic! Imbalance compensation In the event of imbalance, Fuzzy Logic immediately calculates the maximum possible speed, sets this speed and starts spinning. This provides optimum utilization of the spinning time at full speed. Needless to say that the residual dampness results are much better than with conventional spinning. Because here an imbalance results in much time being wasted through attempts to loosen the laundry, followed only by spinning at a reduced speed. Washing without wasting - with automatic water level adjustment Fuzzy automatic water level adjustment adapts water and energy consumption to the individual requirements of each wash programme, depending on the amount of laundry and type of fabric. The washing machine will allow only as much water to enter as is really needed for the loaded amount. And less water will heat up quicker - which means less energy consumption. So you can now wash small amounts of laundry more economically.

Words underlined in red for emphasis – not present in the original advertisement

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Rule n: if u(k-1)=An and y(k-1)=Bn then yn(k)=-a1ny(k-1)+b1nu(k-1)+cnn

Rule 1: if u(k-1)=A1 and y(k-1)=B1 then y1(k)=-a11y(k-1)+b11u(k-1)+c11

Rule 2: if u(k-1)=A2 and y(k-1)=B2 then y2(k)=-a12y(k-1)+b12u(k-1)+c22

Applications:Applications:(Fuzzy Takagi-Sugeno Controller)

… …

Fuzzy clustering Weighted instrumental variable

Sinyal kendali

u(k)

Sinyal acuan

r(k)

Sinyal keluarany(k)

-

Model Fuzzy TS

Model Inverse fuzzy TS

Low pass filter

Pressure Process Rig

ŷ(k)

+

+-

Pengendali Multi Model Lokal Linier

Pengendali #1

Pengendali #2

Pengendali #n

fuzzy scheduling

Model lokal linier #1

Model lokal linier #2

Model lokal linier #n

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Hardware RequirementHardware Requirement

Process Interface (Feedback 38-200)

Pressure Process Rig (Feedback 38-714)

DAC dan ADC (Waktu pencuplikan 0,15 detik)Komputer

4-20 mA

4-20 mA

0,4-2 V0,4-2 V

4-20 mA4-20 mA

Process output

Control signal

V/Iconverter

I/Vconverter

Process Interface (Feedback 38-200)Process Interface (Feedback 38-200)

Pressure Process Rig (Feedback 38-714)Pressure Process Rig (Feedback 38-714)

DAC dan ADC (Waktu pencuplikan 0,15 detik)Komputer

4-20 mA4-20 mA

4-20 mA4-20 mA

0,4-2 V0,4-2 V0,4-2 V0,4-2 V

4-20 mA4-20 mA4-20 mA4-20 mA

Process outputProcess output

Control signal

V/Iconverter

I/Vconverter

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Pengumpulan DataPengumpulan DataData fluktuasi harga saham PT CPIN

Banyak Data : 483Data untuk identifikasi: 243 (th 2002 + th 2001)Data untuk validasi silang: 240 (th 2003)

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ReferencesReferences• J. Yen and R. Langari, “Fuzzy Logic: Intelligence,

Control, and Information,” Prentice Hall, 1999• Timothy J.Ross, “Fuzzy Logic With Engineering

Applications”, McGraw Hill Inc. 1995• L.H. Tsoukalas and R.E. Uhrig, “Fuzzy and Neural

Approaches in Engineering,” Wiley, 1997• M.M. Gupta, L. Jin, and N. Homma, “Static and Dynamic

Neural Networks: From Fundamentals to Advanced Theory,” IEEE-Press, 2003

• J.W. Hines, “Fuzzy and Neural Approaches in Engineering,” Wiley, 1997

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GradingGrading

• Homeworks: 20%• Paper Project: 20%• Mid test: 30%• Final test: tidak ada• Final Paper : 30%