1 fuzzy logic yeni herdiyeni yeni departemen ilmu komputer ipb

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Fuzzy Logic

Yeni Herdiyenihttp://www.cs.ipb.ac.id/~yeniDepartemen Ilmu Komputer IPB

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Contents

Air conditioner (Mitsubishi) Vacuum cleaner (Panasonic) Automatic transmission system (Nissan, Subaru,

Mitsubishi) Washing machine (Matsushita, Hitachi) Camcorder (Panasonic, Sanyo, Fisher, Canon) Other…

Examples taken from the book: Soft Computing and Intelligent System Design, by F.O. Karray & C. de Silva, 2004.

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Air conditioner (Mitsubishi) Sistem pengkondisian udara konvensional menggunakan

pengendali on-off. AC Mitsubishi kontrol dengan menggunakan aturan fuzzy seperti:

"Jika suhu udara semakin hangat, daya pendinginan naik sedikit, jika udara semakin dingin, matikan daya ke bawah"

Mesin menjadi halus sehingga tidak cepat rusak, lebih konsisten suhu kamar yang nyaman, dan peningkatan efisiensi (penghematan energi).

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Vacuum cleaner (Panasonic) Karakteristik lantai dan jumlah debu yang dibaca oleh

sensor inframerah, dan mikroprosesor akan memilih daya yang sesuai dengan kontrol fuzzy berdasarkan karakteristik lantai.

Karakteristik lantai meliputi jenis (kayu, semen, ubin, kelembutan karpet, karpet tebal, dll).

Pola perubahan jumlah debu yang melewati sensor inframerah dapat dideteksi. Mikroprosesor menetapkan pengaturan yang sesuai dengan vakum dan daya motor menggunakan skema kontrol fuzzy.

Lampu merah dan hijau dari penyedot debu menunjukkan jumlah debu tersisa di lantai.

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Automatic transmission system (Nissan, Subaru, Mitsubishi)

Dalam sistem transmisi otomatis konvensional, sensor elektronik mengukur kecepatan kendaraan dan membuka throttle, and gear bergeser berdasarkan nilai-nilai variabel-variabel yang telah ditentukan.

Pada Nissan, tipe sistem ini tidak mampu memberikan performa kontrol seragam yang memuaskan untuk driver karena hanya menyediakan sekitar tiga pola pergeseran yang berbeda.

transmisi kontrol fuzzy mampu membaca beberapa variabel termasuk kecepatan kendaraan dan akselerasi, membuka throttle, laju perubahan pembukaan throttle, beban mesin, dan gaya mengemudi. Ketika variabel ini terdeteksi diberi bobot nilai, dan agregat fuzzy dihitung untuk memutuskan apakah oper.

Kontroler ini dikatakan lebih fleksibel, halus, dan efisien, memberikan kinerja yang lebih baik. Juga, sebuah sistem yang terintegrasi yang dikembangkan oleh Mitsubishi menggunakan logika fuzzy untuk kontrol aktif dari sistem suspensi, four-wheel-drive (traksi), kemudi, dan pendingin udara.

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Washing machine (Matsushita, Hitachi) Sistem kontrol ini dapat

mengendalikan kualitas dan kuantitas kotoran, ukuran beban, dan jenis kain, dan mengatur siklus cuci dan jumlah deterjen sesuai.

Jumlah air di mesin cuci diukur dengan sensor cahaya.

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Camcorder (Panasonic, Sanyo, Fisher, Canon) Video kamera menentukan fokus dan pencahayaan

terbaik, terutama ketika beberapa objek dalam gambar. Juga memiliki image stabilizer untuk mengatasi tangan bergetar. Fuzzy digunakan untuk image stabilizer.

Bingkai gambar saat ini dibandingkan dengan frame sebelumnya dari memori. Sebuah objek biasanya stasioner (misalnya, rumah) diidentifikasi dan pergeseran koordinat dihitung.

Pergeseran ini dikurangi dari gambar untuk mengimbangi pergerakan tangan. Sebuah algoritma fuzzy memberikan kontrol mulus / tindakan kompensasi.

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Other…

Elevator control (Fujitec, Toshiba): A fuzzy scheme evaluates passenger traffic and the elevator variables (load, speed, etc.) to determine car announcement and stopping time. This reduces waiting time and improves the efficiency and reliability of operation.

Handheld computer (Sony): A fuzzy logic scheme reads the hand-written input and interprets the characters for data entry.

Television (Sony): A fuzzy logic scheme uses sensed variables such as ambient lighting, time of day, and user profile, and adjusts such parameters as screen brightness, colour, contrast, and sound.

Antilock braking system (Nissan): The system senses wheel speed, road conditions, and driving pattern, and the fuzzy ABS determines the braking action, with skid control.

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Other…

Subway train (Sendai): A fuzzy decision scheme is used by the subway trains in Sendai, Japan, to determine the speed and stopping routine. Ride comfort and safety are used as performance requirements.

Other applications of fuzzy logic include a hot water heater (Matsushita), a rice cooker (Hitachi), and a cement kiln (Denmark). A fuzzy stock-trading program can manage stock portfolios. A fuzzy golf diagnostic system is able to select the best golf club based on size, characteristics, and swing of a golfer. A fuzzy mug search system helps in criminal investigations by analyzing mug shots (photos of the suspects) along with other input data (say, statements such as "short, heavy-set, and young-looking . . ." from witnesses) to determine the most likely criminal. Gift-wrapped chocolates with fuzzy statements are available for Valentine's Day. Even a Yamaha "fuzzy" scooter was spotted in Taipei.

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Introduction

“Mathematics that refers to reality is not certain and mathematics that is certain does not refer to reality”

Albert Einstein “While the mathematician constructs a theory in terms of ´perfect

´objects, the experimental observes objects of which the properties demanded by theory are and can, in the very nature of measurement, be only approximately true”

Max Black “What makes society turn is science, and the language of science

is math, and the structure of math is logic, and the bedrock of logic is Aristotle, and that is what goes out with fuzzy logic”

Bart Kosko

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Introduction (cont.) Uncertainty is produced when a lack of information exists. The complexity also involves the degree of uncertainty. It is possible to have a great deal of data (facts collected from

observations or measurements) and at the same time lack of information (meaningful interpretation and correlation of data that allows one to make decisions.)

Data Information

Database Intelligent information systems

Knowledge & Intelligence

Knowledge base & AI

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Introduction (cont.)Knowledge is information at a higher level of abstraction.

Ex: Ali is 10 years old (fact) Ali is not old (knowledge)

Our problems are: Decision Management Prediction

Solutions are: Faster access to more information and of increased aid in analysis Understanding – utilizing information available Managing with information not avaliable

Large amount of information with large amount of uncertainty lead to complexity.

Avareness of knowledge (what we know and what we do not know) and complexity goes together.

Ex: Driving a car is complex, driving in an iced road is more compex, since more knowledge is needed for driving in an iced road.

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Introduction (cont.) Fuzzy logic provides a systematic basis for representation of uncertainty,

imprecision, vagueness, and/or incompletenes. Uncertain information: Information for which it is not possible to

determine whether it is true or false. Ex: a person is “possibly 30 years old” Imprecise information: Information which is not available as precise as it

should be. Ex: A person is “around 30 years old.” Vague information: Information which is inherently vague.

Ex: A person is “young.” Inconsistent information: Information which contains two or more

assertions that cannot be true at the same time. Ex: Two assertions are given: “Ali is 16” and “Ali is older than 20”

Incomplete information: information for which data is missing or data is partially available. Ex: A person’s age is “not known” or a person is “between 25 and 32 years old”

Combination of the various types of such information may also exist. Ex: “possibly young”, “possibly around 30”, etc.

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Introduction (cont.)

UNCERTAINTY(Uncertainty-based information)

COMPLEXITY(Description-algorithmic infor.)

CREDIBILITY(relevance)

USEFULNESS

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Example: When uncertainties like heavy traffic, unfamiliar roads, unstable wheather conditions, etc. increase, the complexity of driving a car increases.

How do we go with the complexity? We try to simplify the complexity by making a

satisfactory trade-off between information available to us and the amount of uncertainty we allow.

We increase the amount of uncertainty by replacing some of the precise information with vague but more useful information.

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Examples: Travel directions: try to do it in mm terms (or turn the wheel % 23 left,

etc.), which is very precise and complex but not very useful. So replace mm information with city blocks, which is not as precise but more meaningful (and/or useful) information.

Parking a car: doing it in mm terms, which is very precise and complex but difficult and very costly and not very useful. So replace mm information with approximate terms (between two lines), which is not as precise but more meaningful (or useful) information and can be done in less cost.

Describing wheather of a day: try to do it in % cloud cover, which is very precise and complex but not very useful. So replace % cloud information with vague terms (very cloudy, sunny etc.), which is not as precise but more meaningful (or useful) information.

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Fuzzy logic has been used for two different senses: In a narrow sense: refers to logical system generalizing crisp

logic for reasoning uncertainty. In a broad sense: refers to all of the theories and technologies

that employ fuzzy sets, which are classes with imprecise boundaries.

The broad sense of fuzzy logic includes the narrow sense of fuzzy logic as a branch.

Other areas include fuzzy control, fuzzy pattern recongnition, fuzzy arithmetic, fuzzy probability theory, fuzzy decision analysis, fuzzy databases, fuzzy expert systems, fuzzy computer SW and HW, etc.

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With Fuzzy Logic, one can accomplish two things:

Ease of describing human knowledge involving vague concepts

Enhanced ability to develop a cost-effective solution to real-world

In another word, fuzzy logic not only provides a cost effective way to model complex systems involving numeric variables but also offers a quantitative description of the system that is easy to comprehend.

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Fuzzy Logic was motivated by two objectives:

First, it aims to minimize difficulties in developing and analyzing complex systems encountered by conventional mathematical tools. This motivation requires fuzzy logic to work in quantitative and numeric domains.

Second, it is motivated by observing that human reasoning can utilize concepts and knowledge that do not have well defined, sharp boundaries (i.e., vague concepts). This motivation enables fuzzy logic to have a descriptive and qualitative form. This is related to AI.

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Components of Fuzzy Logic

Fuzzy Predicates: tall, small, kind, expensive,...

Predicates modifiers (hedges): very, quite, more or less, extremely,..

Fuzzy truth values: true, very true, fairly false,...

Fuzzy quantifiers: most, few, almost, usually, ..

Fuzzy probabilities: likely, very likely, highly likely,...

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Applications

Control: “If the temperature is very high and the presure is decreasing rapidly, then reduce the heat significantly.”

Database: “Retrieve the names of all candidates that are fairly young, have a strong background in algorithms, and a modest administrative experience.”

Medicine: Hepatitis is characterized by the statement, ‘Total proteins are usually normal, albumin is decreased, -globulins are slightly decreased, -globulins are slightly decreased, -globulins are increased’

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Probability theory vs fuzzy set theory: Probability measures the likelihood of a future event,

based on something known now. Probability is the theory of random events and is not capable of capturing uncertainty resulting from vagueness of linguistic terms.

Fuzziness is not the uncertainty of expectation. It is the uncertainty resulting from imprecision of meaning of a concept expressed by a linguistic term in NL, such as “tall” or “warm” etc.

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Probability theory vs fuzzy set theory (cont): Fuzzy set theory makes statements about one concrete object;

therefore, modeling local vagueness, whereas probability theory makes statements about a collection of objects from which one is selected; therefore, modeling global uncertainty.

Fuzzy logic and probability complement each other. Example: “highly probable” is a concept that involves both randomness and fuziness.

The behaviour of a fuzzy system is completely deterministic. Fuzzy logic differs from multivalued logic by introducing concepts

such as linguistic variables and hedges to capture human linguistic reasoning.

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Even though the broad sense of fuzzy logic covers a wide range of theories and techniques, its core technique is based on four basic concepts:

Fuzzy sets: sets with smooth boundaries; Linguistic variables: variables whose values are both qualitatively

and quantitatively described by a fuzzy set; Possibility distribution: constraints on the value of a linguistic

variable imposed by assigning it a fuzzy set; and Fuzzy if-then rules: a knowledge representation scheme for

describing a functional mapping (fuzzy mapping rules) or a logical formula that generalizes an implication in two-valued logic (fuzzy implication rules).

The first three concepts are fundamental for all subareas in fuzzy logic, but the fourth one is also important.

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Introduction

Most of the phenomena we encounter everyday are imprecise - the imprecision may be associated with their shapes, position, color, texture, semantics that describe what they are

Fuzziness primarily describes uncertainty (partial truth) and imprecision

The key idea of fuzziness comes from the multivalued logic: Everything is a matter of degree

Imprecision raises in several faces, e.g. as a semantic ambiguity

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Introduction

By fuzzifying crisp data obtained from measurements, FL enhances the robustness of a system

Imprecision raises in several faces - for example, as a semantic ambiguity the statement “the soup is HOT” is ambiguous, but not

fuzzy

e.g. [20º,80º]

The temperature of the soupHot

The amount of spices used

Definition of the domain

of discourse

Transaction to Fuzziness

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The word “fuzzy” can be defined as “imprecisely defined, confused, vague”

Humans represent and manage natural language terms (data) which are vague. Almost all answers to questions raised in everyday life are within some proximity of the absolute truth

empty half-full full? almost full?

or half-empty? nearly full? ……

Does itremainempty?

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Probability vs uncertainty vs Fuzziness Probability theory is one of the most traditional

theories for representing uncertainty in mathematical models

Nature of uncertainty in a problem is a point which should be clearly recognized by engineer - there is uncertainty that arises from chance, from imprecision, from a lack of knowledge, from vagueness, from randomness…

probability theory deals with the expectation of an event (future event, its outcome is not known yet), i.e. it is a theory of random events

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Fuzziness deals with the impression of meaning of concepts expressed in natural language - it is not concerned with events at all

Fuzzy theory handles nonrandom uncertainty

Random

Uncertain Certain

Fuzzy, imprecise, vague

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a Fuzzy System (FS) is defined as a system with operating principles based on fuzzy information processing and decision making

There are several ways to represent knowledge, but the most commonly used has a form of rules:

IF (premise)A THEN (conclusion)B

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From a knowledge representation viewpoint, a fuzzy IF-THEN rule is a scheme for capturing knowledge that involves imprecision - if we know a premise (fact), then we can infer another fact (conclusion)

A fuzzy system (FS) is constructed from a collection of fuzzy IF-THEN rules

Acquisition of knowledge captured in IF-THEN rules is NOT a trivial task (expert knowledge, systems measurements, etc.)

The building blocks for fuzzy IF-THEN rules are FUZZY SETS

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The rule

“IF the air is cool THEN set the motor speed to slow”

has a form:

IF x is A THEN y is B,

where fuzzy sets “cool” and “slow” are labeled by A and B, correspondingly

A and B characterize fuzzy propositions about variables x and y

Most of the information involved in human communication uses natural language terms that are often vague, imprecise, ambiguous by their nature, and fuzzy sets can serve as the mathematical foundation of natural language

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A Fuzzy Set is a set with a smooth boundaries Fuzzy Set Theory generalizes classical set theory to allow

partial membership Fuzzy Set A is a universal set U is determined by a

membership function A(x) that assigns to each element xU a number A(x) in the unit interval [0,1]

Universal set U (Universe of Discourse) contains all possible elements of concern for a particular application

Fuzzy set has a one-to-one correspondence with its membership function

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Fuzzy set A is defined as

A = { (x, A(x)) }, xU, A(x)[0,1]

A(x) = Degree(xA) is a grade of membership of element xU in set A

X1 X2 X3 . 0 1/2 1

.

. unit interval

xN

.

. U (universe of discourse)

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The membership functions themselves are NOT fuzzy - they are precise mathematical functions; once a fuzzy property is represented by a membership function, nothing is fuzzy anymore

Suppose U is the interval [0,85] representing the age of ordinary human beings, and the linguistic term “young” as a function of age (value of the variable age) can be defined as

[see the graphical representation on the next slide]

[ !! pay attention to the usage of the symbol “ / “ ]

x5

25-x1 x

1 young"" A

-185

25

225

0

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If U is a set of integers from 1 to 10 ( U={1,2,…,10} ), then “small” is a fuzzy subset of U, and it can be defined using enumeration (summation notation):

A = “small” = 1/1+1/2+0.85/3+0.75/4+0.5/5+0.3/6+0.1/7

1

25

0 U (universe of discourse) 85

31

0.41

A

(x) Universe of discourse U is

continuos

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In the previous example elements of U (universal set) with zero membership degrees are not included into enumeration

A notion of a fuzzy set provides a convenient way of defining abstraction - a process which plays a basic role in human thinking and communication

All theories that use the basic concept of fuzzy set can be called in a whole Fuzzy Theory

Rough classification of Fuzzy Theory can be depicted as follows [note that dependencies between the branches are not shown] :

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Fuzzy Theory

Fuzzy Fuzzy Uncertainty & Mathematics Decision-Making Information

Fuzzy Systems Fuzzy Logic & AI

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crisp (classical) set A A = set of TALL people fuzzy set A

1.0 1.0

0.65

0.0 0.0

1.75m height 1.75m

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Main types of membership functions (MF):

(a) Triangular MF is specified by 3 parameters {a,b,c}:

(b) Trapezoidal MF is specified by 4 parameters {a,b,c,d}:

c x if 0,

c x b if b),-(cx)-(c

b x a if a),-(ba)-(x

a x if , 0

)c,b,a:x(trn

d x if 0,

d x c if c),-(dx)-(d

c x b if 1,

b x a if a),-(ba)-(x

a x if 0,

d)c,b,a, : x(trp

Membership Functions

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(c) Gaussian MF is specified by 2 parameters {a,}:

(d) Bell-shaped MF is specified by 3 parameters {a,b,}:

(e) Sigmoidal MF is specified by 2 parameters {a,b}:

2

2a) - (x-exp )a, : x(gsn

2b

a - x

1

1 )b,a, : x(bll

b)-a(x-e1

1 b)a, : x(sgm

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