fuzzy logic (vast 2015)
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Dept. of Electronics and Communication Engg.
Vision: Progress through the growing knowledge of Electronics and Communication technology.
Mission: To emerge as a world class center of learning, research and development, integrating with the latest trends
in Electronics and Communication Engineering for the service of humanity.
15.01.2015
Prof. Dr. S. Swapna Kumar
Introduction to FUZZY LOGIC
Professor Dr. Lotfali Asker Zadeh
Born: February 4, 1921 (age 93)
Baku, Soviet, Azerbaijan
Professional affiliationProfessor in the Graduate School, Computer Science Division
Department of Electrical Engineering and Computer Sciences
University of California
Berkeley, CA 94720 -1776
Director, Berkeley Initiative in Soft Computing (BISC)
http://www.cs.berkeley.edu/~zadeh/
Tel.(office): (510) 642-4959
Fax (office): (510) 642-1712
Tel.(home): (510) 526-2569
Fax (home): (510) 526-2433
1938: Alborz International High School, Tehran, Iran.
1942: B.S. engineering degree, University of Tehran, Iran.
1946 : M.S., Massachusetts Institute of Technology.
1949: PhD – (Electrical Engineering}, Columbia University.
Faculty member: Columbia University and the University of
California-Berkeley.
1990: Retired from UC-Berkeley
Director of UC Berkeley Initiative on Soft Computing.2
Adversity
*Fuzzy Logic: Intelligence, Control, and Information - J. Yen and R. Langari, Prentice Hall 1999
1964: Lotfi A. Zadeh, UC Berkeley, introduced the
paper on fuzzy sets.
Idea of grade of membership was born
Sharp criticism from academic community
Name!
Theory’s emphasis on imprecision
Waste of government funds!
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History of Fuzzy Logic
*Fuzzy Logic: Intelligence, Control, and Information - J. Yen and R. Langari, Prentice Hall 1999
1965: Zadeh introduced fuzzy set theory
1970s: research groups were form in JAPAN
1974: Mamdani, United Kingdom, developed the first fuzzy
logic controller
1977: Dubois applied fuzzy sets in a comprehensive study of
traffic conditions
1976-1987: Industrial application of fuzzy logic in Japan and
Europe
1987-Present: Fuzzy Boom4
Precision is not ULTIMATE truth
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Traditional logic
A rose is either RED or not RED.
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Traditional (crisp) logic
What about this rose?
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Precision & Significant in Real world
Fuzzy logic relative importance of precision; when a rough
answer will do.
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What/How……!!!!
FastestSlow FastSlowest[ 0.1 – 0.25 ] [ 0.25 – 0.50 ] [ 0.50 – 0.75 ] [ 0.75 – 1.00 ]
Very tall ~ 7f
Tall ~ 6f
Average ~ 5f
Short ~ 4f
Very short ~ 3f9
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What is FUZZY LOGIC?
Fuzzy logic:
A way to represent variation or imprecision in logic
A way to make use of natural language in logic
Approximate reasoning
Linguistic variables:
Temp: {freezing, cool, warm, hot}
Cloud Cover: {overcast, partly cloudy, sunny}
Speed: {slow, fast}
Problem-solving methodology
Definite conclusion
Fuzzy Sets
NOTE: FUZZY SET IS NOT A “SET” but is a mapping
A x x x XA {( , ( ))| }
Universe or
universe of discourseFuzzy set
Membership
Function (MF)
A fuzzy set is totally characterized by a
membership function (MF).
Integer
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Membership function
A membership function (MF) is a curve that maps input space
to a membership value between 0 and 1.
cxif
cxbifbc
xc
bxaifab
ax
axif
xA
0
0
)(
a b c x
µA(x)
1
0
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Membership Functions (MFs)
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Is water colorless?
CRISP Yes = 1, No = 0
Is I am honest?
Extremely honest = 1
Very honest = 0.80
Honest at times = 0.4
Extremely dishonest = 0
Crisp Vs.. Fuzzy
Membership Functions
Fuzzy logic Connectives:
Fuzzy Disjunction,
Fuzzy Conjunction,
1550 70 90 1103010
Temp. (F°)
Freezing Cool Warm Hot
0
1
0.7
0.3
How cool is 36 F° ?
µA(x)
Michio SugenoEbrahim Mamdani
Fuzzy Logic System
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Crisp Input
Fuzzification
Rules
De-Fuzzification
Crisp Output Result
“antecedent”
“consequent”
Begin
End
FUZZY LOGIC USING MATLAB
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PRIMARY GUI TOOLS
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User Interface Layout: FIS Editor
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User Interface Layout: MF Editor
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User Interface Layout: MF Editor
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User Interface Layout: Rule Editor
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User Interface Layout: Rule Viewer
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fis=readfis('ws')
out=evalfis(scale,fis)
out=result
UIL: Surface Viewer
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Fuzzy Logic Control of
Washing Machines
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BWA
Fuzzy Surface
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Drawbacks to Fuzzy logic
Requires tuning of membership functions
Fuzzy Logic control may not scale well to large or
complex problems
Deals with imprecision, and vagueness, but not
uncertainty
Fuzzy Logic Applications
Aerospace
Automotive
Business
Chemical Industry
Defense
Electronics
Financial
Industrial
Manufacturing
Marine
Medical
Signal Processing
Telecommunications
Transportation
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Summary
Fuzzy logic provides an alternative way to represent
linguistic and subjective attributes of the real world in
computing.
It is able to be applied to control systems and other
applications in order to improve the efficiency and
simplicity of the design process.
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References
L. Zadah, “Fuzzy sets as a basis of possibility” Fuzzy
Sets Systems, Vol. 1, pp3-28, 1978.
T. J. Ross, “Fuzzy Logic with Engineering
Applications”, McGraw-Hill, 1995.
K. M. Passino, S. Yurkovich, "Fuzzy Control" Addison
Wesley, 1998.
Google…..
Questions
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