L T P C PURPOSE INSTRUCTIONAL OBJECTIVES

architecture, Rule based Systems, Associative nets and symbolic computing. building an expert system Building an Expert System, Difficulties in develo...

0 downloads 61 Views 145KB Size
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 set-vagueness – uncertainty and imprecision – fuzzy set-fuzzy operators – properties – crisp versus fuzzy sets-representation of fuzzy sets-fuzzy complements, union, interactioncombination of operators, crisp and fuzzy relations – compositions of fuzzy relations.

FUZZY LOGIC AND CONTROLLERS Fuzzy logic-classical logic-fuzzy propositions and quantifiers – linguistic hedges – fuzzification and its types-defuzzification 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 analysis-control system-inverted 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 information-Pearson’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 : Jul-Nov 2011 Location :

ESB BLOCK

Course timings: Day

x

Section

HR

TIMING

MONDAY

1 4

8.45-9.35am 11.25-12.15p.m -

TUESDAY

-

WEDNESDAY

5 -

1.30-2.20pm -

THURSDAY 8.45-9.35am 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 information-Pearson’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 TEST-I UNIT-I&II

1 hr 30 minutes

2

14.09.11

3

31.10.11

CYCLE TESTII

UNIT-III & 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 3-8 4 25-29 14 152-161, 16-20 3 20-23 8 80-88, 95-107 10 112-115, 116-117 10 118-120, 121-123

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 20-23

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

12 128-141

Difficulties in development of ES

16 179-185

Donald A. Common pitfalls, Waterman, pitfalls during development A guide to Expert Expert systems in System, market place, Addison Wiley, 1999. Commercial implications

17 186-199

20 201-206

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

21 212-217

INTRODUCTION OF FUZZY SETS AND RELATIONS Crisp set-vagueness – uncertainty and imprecision – fuzzy set-fuzzy operators – properties – crisp versus fuzzy setsrepresentation of fuzzy sets-fuzzy complements, union, intersection-combination 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 26-28

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 28-32

23

crisp relations (problems)

3 46-50

24

fuzzy relations (problems)

3 52-55

compositions of fuzzy relations (problems)

3 60-86

18 19

20

21 22

25 26 27

1-9,12,1724

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 28-32 1,2 15-17 35-46

FUZZY LOGIC AND CONTROLLERS Fuzzy logic-classical logic-fuzzy 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

7 183-196

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.

7 199-212

34

defuzzification methods

5 130-146

35

Problems defuzzification

for

5 147-150

36

data base – rule base – inference engine structure of FLC

13 475-477

30 31 32

33

8 236-239 4 87-117

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

4 126-130 (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 analysis-control system-inverted 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 13 483-489 13 490-495

(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.