EE 0455
EXPERT SYSTEM AND FUZZY LOGIC
L
T P C
3
0
0 3
Prerequisite Nil PURPOSE To learn the concept of expert system and fuzzy logic along with engineering applications.
INSTRUCTIONAL OBJECTIVES At the end of course the students will be able to:
Understand expert systems and their tools with methodology for building expert system. Understand fuzzy logic basics and operations, Fuzzy arithmetic and representations and classical logic. Apply fuzzy logic for engineering problems. EXPERT SYSTEMS – INTRODUCTION & TOOLS Introduction, Characteristics, Acquiring, representing knowledge reasoning. Nature of ES tools, stages in development of ES tools. EMYCIN, EXPERT, OPSS, ROSIE, Block board architecture, Rule based Systems, Associative nets and symbolic computing.
BUILDING AN EXPERT SYSTEM Building an Expert System, Difficulties in development of ES, Common pitfalls, pitfalls during development, Expert systems in market place, commercial implications.
INTRODUCTION OF FUZZY SETS AND RELATIONS Crisp setvagueness – uncertainty and imprecision – fuzzy setfuzzy operators – properties – crisp versus fuzzy setsrepresentation of fuzzy setsfuzzy complements, union, interactioncombination of operators, crisp and fuzzy relations – compositions of fuzzy relations.
FUZZY LOGIC AND CONTROLLERS Fuzzy logicclassical logicfuzzy propositions and quantifiers – linguistic hedges – fuzzification and its typesdefuzzification methods – data base – rule base – inference engine structure of FLC.
APPLICATIONS OF EXPERT SYSTEMS AND FUZZY LOGIC. Applications of expert systems and fuzzy logic In ac and dc drives. VAR control, contingency analysiscontrol systeminverted pendulum and aircraft control application.
TEXT BOOKS 1. 2.
Timothy J.Ross, Fuzzy Logic with Engineering Applications, International edition, McGraw Hill, 2000. Donald A. Waterman, A guide to Expert System, Addison Wiley, 1999.
REFERENCE BOOKS 1. 2.
Dan W.Patterson, Introduction to AI and expert systems, Pearson education. John yen and Reza lansari, Fuzzy logic. Fuzzy logic intelligence, control and informationPearson’s education. EE 0455  EXPERT SYSTEM AND FUZZY LOGIC (SE)
Course designed by 1
Department of Electrical and Electronics Engineering
Student outcomes
a
b
c
x
2
Category
General (G)
d
e
f
x
Basic Sciences (B)
Engineering Sciences and Technical Arts (E)
g
h
i
x
j
k
x
x
Professional Subjects (P)
x
3
Broad area (for ‘P’category
Electrical Circuit and machines systems
Electronics
Power Systems
Intelligent systems x
4
Course Coordinator
Ms.D.Suchitra
Mapping of Program Instructional Objectives Vs Program Outcomes
Program Outcomes
Program Instructional objectives Understand expert systems and their tools with methodology for building expert system.
(a)
an ability to apply knowledge of
Understand fuzzy logic Apply fuzzy logic basics and operations, for engineering Fuzzy arithmetic and problems. representations and classical logic.
x
x
x
x
mathematics,science and engineering (e)
(h)
an ability to identify, formulate, and solve engineering problems the broad education necessary to
understand the impact of engineering solutions in a global perspective. (j)
x x
a knowledge of contemporary issues x
(k)
x
an ability to use the techniques, skills, and modern engineering tools necessary for engineering practice.
x
SRM UNIVERSITY FACULTY OF ENGINEERING AND TECHNOLOGY SCHOOL OF ELECTRONICS AND ELECTRICAL ENGINEERING DEPARTMENT OF EEE Course Code : EE0455 Course Title : EXPERT SYSTEM AND FUZZY LOGIC Semester :
VII
Course Time : JulNov 2011 Location :
ESB BLOCK
Course timings: Day
x
Section
HR
TIMING
MONDAY
1 4
8.459.35am 11.2512.15p.m 
TUESDAY

WEDNESDAY
5 
1.302.20pm 
THURSDAY 8.459.35am FRIDAY
1
Faculty Details Sec.
Name of the Staff
Common D.SUCHITRA to all sections
Office
Office hour
Mail id
ESB BLOCK FIRST FLOOR
8.45AM4.00PM
[email protected]
Required Text Books: 1. Timothy J.Ross, Fuzzy Logic with Engineering Applications, International edition, McGraw Hill, 2000. 2.
Donald A. Waterman, A guide to Expert System, Addison Wiley, 1999.
Reference Books 1. 2.
Dan W.Patterson, Introduction to AI and expert systems, Pearson education. John yen and Reza lansari, Fuzzy logic. Fuzzy logic intelligence, control and informationPearson’s education. Resources : o www.ieeexplorer.com Prerequisite :Nil
INSTRUCTIONAL OBJECTIVES: After the completion of this course successfully the students will be able to:
Understand expert systems and their tools with methodology for building expert system. Understand fuzzy logic basics and operations, Fuzzy arithmetic and representations and classical logic. Apply fuzzy logic for engineering problems. Assessment Details Cycle Test – I
:
10 Marks
Surprise Test
:
05 Marks
Cycle Test – II :
10 Marks
Model Exam
:
20 Marks
Attendance
:
05 Marks
Total
Test Schedule
:
50 Marks
S. No
COMMENCEMENT
1
3.08.11
TEST
TOPICS
DURATION
DATE
CYCLE TESTI UNITI&II
1 hr 30 minutes
2
14.09.11
3
31.10.11
CYCLE TESTII
UNITIII & IV
MODEL EXAM
ALL 5 UNITS
1 hr 30 minutes
3 hrs
Outcomes Student who have successfully completed this course, Instructional Objective Understand expert systems and their tools with methodology for building expert system. Understand fuzzy logic basics and operations, Fuzzy arithmetic and representations and classical logic. Apply fuzzy logic for engineering problems.
Program outcome a).An ability to apply knowledge of mathematics, science, and engineering.
e).An ability to identify, formulate, and solve engineering problems.
(h) the broad education necessary to understand the impact of engineering solutions in a global perspective.
(j) a knowledge of contemporary issues
(k) an ability to use the techniques, skills, and modern engineering tools necessary for engineering practice.
Session Plan
EXPERT SYSTEMS – INTRODUCTION & TOOLS Introduction, Characteristics, Acquiring, representing knowledge reasoning. Nature of ES tools, stages in development of ES tools. EMYCIN, EXPERT, OPSS, ROSIE, Block board architecture, Rule based Systems, Associative nets and symbolic computing. Sessio n No.
Topics to be covered
Text book
1
Introduction
2
Characteristics
3
Acquiring and organizing knowledge Donald A. Representing knowledge Waterman,
4
reasoning
6
A guide to Nature of ES tools States Expert in development of ES System, Addison tools Wiley, 1999. EMYCIN, EXPERT
7
OPS5, ROSIE
8
Black board architecture
5
Chap.no & Page No. 1 38 4 2529 14 152161, 1620 3 2023 8 8088, 95107 10 112115, 116117 10 118120, 121123
Instructional
Program Outcome
Objective
Understand expert systems and their tools with methodology for building expert system.
(h) the broad education necessary to understand the impact of engineering solutions in a global perspective.
(j) a knowledge of contemporary issues
3 2023
Rule based Systems 9
Rule based Systems
10
Associative nets and symbolic computing
BUILDING AN EXPERT SYSTEM Building an Expert System, Difficulties in development of ES, Common pitfalls, pitfalls during development, Expert systems in market place, commercial implications. Session No. 11
12
13
14
15
Topics to be covered
Text book
Chap.no & Page No.
Building an Expert System
12 128141
Difficulties in development of ES
16 179185
Donald A. Common pitfalls, Waterman, pitfalls during development A guide to Expert Expert systems in System, market place, Addison Wiley, 1999. Commercial implications
17 186199
20 201206
Instructional
Program Outcome
Objective
Understand expert systems and their tools with methodology for building expert system.
(h) the broad education necessary to understand the impact of engineering solutions in a global perspective.
(j) a knowledge of contemporary issues
21 212217
INTRODUCTION OF FUZZY SETS AND RELATIONS Crisp setvagueness – uncertainty and imprecision – fuzzy setfuzzy operators – properties – crisp versus fuzzy setsrepresentation of fuzzy setsfuzzy complements, union, intersectioncombination of operators, crisp and fuzzy relations – compositions of fuzzy relations. Session No.
Topics to be covered
Crisp
set
Text book
–
Chap.no & Page No. 1,2
Instructional
Program Outcome
Objective a).An ability to
– and
16
vagueness uncertainty imprecision
17
fuzzy set – fuzzy operators
2 2628
Properties of fuzzy set, crisp versus fuzzy sets Timothy representation of J.Ross, Fuzzy Logic fuzzy sets with Problems Engineering Applications, complements, International union, intersection – edition, combination of McGraw operators & Hill, 2000. problems related to it.
2 2832
23
crisp relations (problems)
3 4650
24
fuzzy relations (problems)
3 5255
compositions of fuzzy relations (problems)
3 6086
18 19
20
21 22
25 26 27
19,12,1724
apply knowledge of mathematics, science, and engineering. Understand fuzzy logic basics and operations, Fuzzy arithmetic and representations and classical logic.
e).An ability to identify, formulate, and solve engineering problems
(j) a knowledge of contemporary issues
2 2832 1,2 1517 3546
FUZZY LOGIC AND CONTROLLERS Fuzzy logicclassical logicfuzzy propositions and quantifiers – linguistic hedges – fuzzification and its typesdefuzzification methods – data base – rule base – inference engine structure of FLC. Session No.
Topics to be covered
Text book
Chap.no & Page
Instructional
Program Outcome
No. 28
Fuzzy logicclassical logic fuzzy propositions
7 183196
29
Quantifiers, problems related to propositions and Timothy quantifiers. J.Ross, linguistic hedges Fuzzy Logic with Problems Engineering fuzzification and its Applications, International types edition, examples related to McGraw fuzzification Hill, 2000. methods.
7 199212
34
defuzzification methods
5 130146
35
Problems defuzzification
for
5 147150
36
data base – rule base – inference engine structure of FLC
13 475477
30 31 32
33
8 236239 4 87117
Objective a).An ability to apply knowledge of mathematics, science, and engineering.
Understand fuzzy logic basics and operations, Fuzzy arithmetic and representations and classical logic.
e).An ability to identify, formulate, and solve engineering problems
4 126130 (j) a knowledge of contemporary issues
APPLICATIONS OF EXPERT SYSTEMS AND FUZZY LOGIC. Applications of expert systems and fuzzy logic In ac and dc drives. VAR control, contingency analysiscontrol systeminverted pendulum and aircraft control application. Session No. 37
Topics to be covered Applications
Text book
of
Chap.no & Page No. IEEE paper
Instructional
Program Outcome
Objective a).An ability to
expert systems and fuzzy logic In ac drives 38
39 40 41
42 43 44 45
Applications of expert systems and fuzzy logic In dc Timothy drives J.Ross, VAR control Fuzzy Logic with Engineering Contingency Applications, analysis International edition, Control system McGraw Hill, 2000. Inverted pendulum aircraft control application
IEEE paper
IEEE paper Apply fuzzy logic for engineering problems.
apply knowledge of mathematics, science, and engineering.
e)An ability to identify, formulate, and solve engineering problems
IEEE paper
13 478, IEEE paper 13 483489 13 490495
(h) the broad education necessary to understand the impact of engineering solutions in a global perspective.
(j) a knowledge of contemporary issues
(k) an ability to use the techniques, skills, and modern engineering tools necessary for engineering practice.