Download CS2053

FT/GN/68/00/21.04.15
SRI VENKATESWARA COLLEGE OF ENGINEERING
COURSE DELIVERY PLAN - THEORY
Page 1 of 6
LP: CS2053
Department of Electronics and Communication Engineering
B.E
: ECE
PG Specialisation
: NA
Sub. Code / Sub. Name
: CS 2053 SoftComputing
Unit
: I Fuzzy Set Theory
Rev. No: 00
Date:30.06.2015
Regulation: 2008
Unit Syllabus: Introduction to Neuro – Fuzzy and Soft Computing – Fuzzy Sets – Basic Definition and
Terminology – Set-theoretic Operations – Member Function Formulation and Parameterization – Fuzzy Rules
and Fuzzy Reasoning – Extension Principle and Fuzzy Relations – Fuzzy If-Then Rules – Fuzzy Reasoning –
Fuzzy Inference Systems –Mamdani Fuzzy Models – Sugeno Fuzzy Models – Tsukamoto Fuzzy Models –
Input Space Partitioning and Fuzzy Modeling.
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Objective: students learn to apply fuzzy logic and reasoning to
Session
No *
Topics to be covered
Ref
Teaching
Aids
1
Overview on course syllabus;
Introduction to Neuro – Fuzzy and Soft Computing;
1-ch1; pg.1to3
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2
From Conventional AI to Computational Intelligence
1- ch1;pg.3to9
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3
Fuzzy Sets: Basic Definition and Terminology
4
Set-theoretic Operations
5
Member Function Formulation and Parameterization
6
Fuzzy Rules and Fuzzy Reasoning: Extension Principle
7
Fuzzy Relations
8
Fuzzy If-Then Rules
9
Fuzzy Reasoning, Introduction to Fuzzy Inference Systems(FIS)
10
Mamdani Fuzzy Models
1-ch3;pg.74-81
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11
Sugeno Fuzzy Models
1-ch3;pg.81-84
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1-ch3;pg.84-89
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Tsukamoto Fuzzy Models – Input Space Partitioning and Fuzzy
Modeling.Revision
Content beyond syllabus covered (if any):
MATLAB simulation of fuzzy systems, Ref. 9
12
1-ch2;pg.13-21
4-ch2;pg.34-41
1- ch2;pg.21-24
4- ch2;pg.53-57
1- ch2;pg.24-42
,
4- ch4;pg.91-99
1-ch3;pg.47-50
4- ch5;pg.134-141
1-ch3;pg.50-54
4- ch2;pg.58-74
1-ch3;pg.54-62
4-ch5;pg.148-150
1-ch3;pg.62-74
Course Outcome 1:
Apply fuzzy logic and reasoning to handle uncertainty and solve engineering problems.
* Session duration: 50 mins
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FT/GN/68/00/21.04.15
SRI VENKATESWARA COLLEGE OF ENGINEERING
COURSE DELIVERY PLAN - THEORY
Page 2 of 6
Sub. Code / Sub. Name: CS 2053 SoftComputing
Unit : II Optimization
Unit Syllabus :
Derivative-based Optimization – Descent Methods – The Method of Steepest Descent – Classical Newton’s
Method – Step Size Determination – Derivative-free Optimization – Genetic Algorithms – Simulated
Annealing – Random Search – Downhill Simplex Search.
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Objective: To know about the components of various derivative-based and derivative-free optimization
techniques.
Session
No *
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14
15
16
17
18
19
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Teaching
Aids
Topics to be covered
Ref
Introduction to Derivative-based Optimization: Descent Methods
1-ch6; pg.129to133
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The Method of Steepest Descent, Classical Newton’s Method
1-ch6; pg.133to139
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Step Size Determination
1-ch6; pg.141to148
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Introduction to Derivative-free Optimization: Genetic Algorithms
1-ch7; pg.173to180
6-ch8; pg.225to236
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Genetic Algorithms (Cont.)
6-ch8; pg.236to250
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Simulated Annealing
1-ch7; pg.181to186
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Random Search
1-ch7; pg.186to189
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Downhill Simplex Search Revision
1-ch7; pg.189to193
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CAT-I
Content beyond syllabus covered (if any): -
Course Outcome 2:
Apply genetic algorithms to combinatorial optimization problems.
* Session duration: 50 mins
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FT/GN/68/00/21.04.15
SRI VENKATESWARA COLLEGE OF ENGINEERING
COURSE DELIVERY PLAN - THEORY
Page 3 of 6
Sub. Code / Sub. Name: CS 2053 SoftComputing
Unit : IV Neuro Fuzzy Modeling
Unit Syllabus:
Adaptive Neuro-Fuzzy Inference Systems – Architecture – Hybrid Learning Algorithm – Learning Methods
that Cross-fertilize ANFIS and RBFN – Coactive Neuro Fuzzy Modeling – Framework Neuron Functions
for Adaptive Networks – Neuro Fuzzy Spectrum.
9
Objective: To gain insight onto Neuro Fuzzy modeling and control.
Session
No *
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Ref
Teaching
Aids
Basics of Artificial Neural network (ANN)- Perceptron
1-ch9;pg:226-230
6-ch2;pg:11-30
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22
ANN learning, Back propogation algorithm,etc.
1-ch9;pg:233-238
6-ch3;pg:34-53
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Introduction to Adaptive Neuro-Fuzzy Inference Systems (ANFIS)
1-ch12;pg:335-336
24
ANFIS architecture
1-ch12;pg:336-340
25
Hybrid Learning Algorithm – Learning Methods that Cross-fertilize
ANFIS and RBFN
1-ch12;pg:341-342
26
Coactive Neuro Fuzzy Modeling – Introduction and Framework
1-ch13;pg:369-372
27
Neuron Functions for Adaptive Networks
1-ch13;pg:372-376
28
Neuron Functions for Adaptive Networks
1-ch13;pg:376-382
29
Neuro Fuzzy Spectrum. Revision
1-ch13;pg:382-392
Topics to be covered
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Content beyond syllabus covered (if any):
Neuro-fuzzy system applications, Ref. 9
Course Outcome 3:
Provide detailed theoretical and practical aspects of intelligent modeling to integrate the various soft computing
techniques.
* Session duration: 50 mins
FT/GN/68/00/21.04.15
SRI VENKATESWARA COLLEGE OF ENGINEERING
COURSE DELIVERY PLAN - THEORY
Page 4 of 6
Sub. Code / Sub. Name: CS 2053 SoftComputing
Unit : III
Artificial Intelligence
Unit Syllabus:
Introduction, Knowledge Representation – Reasoning, Issues and Acquisition: Prepositional and Predicate
Calculus Rule Based knowledge Representation Symbolic Reasoning Under Uncertainity Basic knowledge
Representation Issues Knowledge acquisition – Heuristic Search: Techniques for Heuristic search Heuristic
Classification -State Space Search: Strategies Implementation of Graph Search Search based on Recursion
Patent -directed Search Production System and Learning.
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Objective: To explore the different paradigms in knowledge representation ,reasoning, familiarize with
propositional and predicate logic and their roles in logic programming;
Session
No *
30
Topics to be covered
Ref
31
Introduction to Knowledge Representation: Reasoning, Issues and
Acquisition - Prepositional Calculus
Predicate Calculus
32
Rule Based knowledge Representation
33
Symbolic Reasoning Under Uncertainity
34
Basic knowledge Representation Issues
2-ch2;pg:33-39
3-ch4;105-129
2-ch2;pg:40-44
3-ch4;131-165
2-ch2;pg:45-57
3-ch4;171-188
2-ch2;pg:62-67
3-ch4;195-211
2-ch2;pg:67-73
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Knowledge acquisition
2-ch2;pg:80-83
CAT-II
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Heuristic Search: Techniques for Heuristic search
37
State Space Search: Strategies
2-ch3;pg:92-118
3-ch4;63-94
2-ch4;pg:123-130
38
Implementation of Graph Search
2-ch4;pg:131-137
39
Search based on Recursion;
2-ch4;pg:138-142
40
Pattern directed Search
2-ch4;pg:142-145
41
Production Systems
2-ch4;pg:145-149
42
Learning Revision
2-ch4;pg:149-155
Content beyond syllabus covered (if any):-
Course Outcome 4:
Learn to apply and integrate various artificial intelligence techniques in intelligent system development.
* Session duration: 50 mins
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FT/GN/68/00/21.04.15
SRI VENKATESWARA COLLEGE OF ENGINEERING
COURSE DELIVERY PLAN - THEORY
Page 5 of 6
Sub. Code / Sub. Name: CS 2053 SoftComputing
Unit : V Applications of Computational Intelligence
Unit Syllabus:
Printed Character Recognition – Inverse Kinematics Problems – Automobile Fuel Efficiency Prediction –
Soft Computing for Color Recipe Prediction
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Objective: Recognize the feasibility of applying a soft computing methodology for a particular problem.
Session
No *
43
Topics to be covered
Ref
Printed Character Recognition
1-ch19; pg.503 to 506
44
Printed Character Recognition (Cont.,)
1-ch19; pg.503 to 506
45
Inverse Kinematics Problems
1-ch19; pg.507to 510
46
Inverse Kinematics Problems (Cont.,)
1-ch19; pg.507to 510
9
47
Automobile Fuel Efficiency Prediction
1-ch19; pg.510 to 513
48
Automobile Fuel Efficiency Prediction (Cont.,)
1-ch19; pg.510 to 513
9
49
Soft Computing for Color Recipe Prediction
1-ch22; pg.568 to576
50
Soft Computing for Color Recipe Prediction (Cont.,). Revision
1-ch22; pg.577 to584
CAT-III
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Content beyond syllabus covered (if any):-
Course Outcome 5:
Prepare the students for developing intelligent modeling, optimization and control of non-linear systems through
case studies.
* Session duration: 50 mins
FT/GN/68/00/21.04.15
SRI VENKATESWARA COLLEGE OF ENGINEERING
COURSE DELIVERY PLAN - THEORY
Page 6 of 6
Sub Code / Sub Name: CS 2053 SoftComputing
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TEXT BOOK:
1. J.S.R.Jang, C.T.Sun and E.Mizutani, “Neuro-Fuzzy and Soft Computing”, PHI, 2004,Pearson Education
2004.
2. N.P.Padhy, “Artificial Intelligence and Intelligent Systems”, Oxford University Press, 2006
REFERENCES:
3. Elaine Rich & Kevin Knight, Artificial Intelligence, Second Edition, Tata Mcgraw Hill Publishing
Comp., 2006, New Delhi.
4. Timothy J.Ross, “Fuzzy Logic with Engineering Applications”, McGraw-Hill, 1997.
5. Davis E.Goldberg, “Genetic Algorithms: Search, Optimization and Machine Learning”, Addison
Wesley, N.Y., 1989.
6. S. Rajasekaran and G.A.V.Pai, “Neural Networks, Fuzzy Logic and Genetic Algorithms”, PHI, 2003.
7. R.Eberhart, P.Simpson and R.Dobbins, “Computational Intelligence - PC Tools”, AP Professional,
Boston, 1996
8. Amit Konar, “Artificial Intelligence and Soft Computing Behaviour and Cognitive model of the
human brain”, CRC Press, 2008.
9. Relevant materials from the internet websites.
Prepared by
Approved by
Signature
Dr. S.Ganesh Vaidyanathan
Designation
Dr. S.Ganesh Vaidyanathan &
K.Thaiyalnayaki
Prof. HoD-EC & Assoc. Prof.-EC
Date
30.06.2015
30.06.2015
Name
HoD-EC
Remarks *:
Remarks *:
* If the same lesson plan is followed in the subsequent semester/year it should be mentioned and signed
by the Faculty and the HOD