DOC/LP/01/28.02.02
LESSON PLAN
LP – CS 2053
LP Rev. No: 00
Sub Code & Name: CS 2053 – SOFT COMPUTING
Unit: I
UNIT I
Branch: EC
Date: 30/06/2014
Semester: VII
Page 01 of 06
FUZZY SET THEORY
10
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.
Objective:
To learn the key aspects of Soft computing and to study the fuzzy logic components
Session
No
Topics to be covered
Time
Ref
Teaching
Aids
1.
Overview on course syllabus;
Introduction to Neuro – Fuzzy and Soft Computing;
50m
1-9
BB
2.
From Conventional AI to Computational Intelligence
50m
1-9
BB
3.
Fuzzy Sets: Basic Definition and Terminology
50m
1,4,6,9
BB/ICT
4.
Cont., Set-theoretic Operations
50m
1,4,6
BB
5.
Member Function Formulation and Parameterization
50m
1,4,6
BB
6.
Fuzzy Rules and Fuzzy Reasoning: Extension Principle
50m
1,4,6
BB
7.
Fuzzy Relations
50m
1,4,6
BB
8.
Fuzzy If-Then Rules
50m
1,4,6
BB
9.
Fuzzy Reasoning, Introduction to Fuzzy Inference
Systems(FIS)
50m
1,4,6
BB
10.
Mamdani Fuzzy Models
50m
1,4,6
BB
11.
Sugeno Fuzzy Models
50m
1,4,6
BB
12.
Tsukamoto Fuzzy Models – Input Space Partitioning
and Fuzzy Modeling.Revision
50m
1,4,6
BB
DOC/LP/01/28.02.02
LESSON PLAN
LP – CS 2053
LP Rev. No: 00
Sub Code & Name: CS 2053 – SOFT COMPUTING
Unit: II
Branch: EC
Date: 30/06/2014
Semester: VII
Page 02 of 06
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.
Objective:
To know about the components of various derivative-based and derivative-free optimization
techniques.
Session
No
13
14
15
16
17
18
19
20
Topics to be covered
Introduction to Derivative-based Optimization:
Descent Methods
The Method of Steepest Descent, Classical
Newton’s Method
Step Size Determination
Introduction to Derivative-free Optimization:
Genetic Algorithms
Genetic Algorithms (Cont.)
Simulated Annealing
CAT-I
Random Search
Downhill Simplex Search. Revision
Time
Ref
Teaching
Aids
50m
1,6
BB
50m
1,6
BB
50m
1,6
BB
50m
1,5,6,9
BB/ICT
50m
50m
90m
50m
50m
1,5,6,9
1,6
1,6
1,6
BB/ICT
BB
BB
BB
DOC/LP/01/28.02.02
LESSON PLAN
LP – CS 2053
LP Rev. No: 00
Sub Code & Name: CS 2053 – SOFT COMPUTING
Unit: IV
Branch: EC
Date: 30/06/2014
Semester: VII
UNIT IV NEURO FUZZY MODELING
Page 03 of 06
9
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.
Objective:
To gain insight onto Neuro Fuzzy modeling and control.
Session
No
21
22
23
24
Topics to be covered
Basics of Artificial Neural network (ANN)Perceptron,
ANN learning, Back propogation algorithm,etc.
Introduction to Adaptive Neuro-Fuzzy Inference
Systems (ANFIS)
ANFIS architecture
Time
Ref
Teaching
Aids
50m
1,6,9
BB/ICT
50m
1,6,9
BB
50m
1,6
BB/ICT
50m
1,6
BB
50m
1,6
BB
50m
1,6
BB
50m
1,6
BB
27
Hybrid Learning Algorithm – Learning Methods
that Cross-fertilize ANFIS and RBFN
Coactive Neuro Fuzzy Modeling – Introduction and
Framework
Neuron Functions for Adaptive Networks
28
Neuron Functions for Adaptive Networks
50m
1,6
BB
29
Neuro Fuzzy Spectrum. Revision
50m
1,6
BB
25
26
DOC/LP/01/28.02.02
LESSON PLAN
LP – CS 2053
LP Rev. No: 00
Sub Code & Name: CS 2053 – SOFT COMPUTING
Unit: III
Branch: EC
Date: 30/06/2014
Semester: VII Page 04 of 06
UNIT III ARTIFICIAL INTELLIGENCE
10
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.
Objective:
To understand the features of artificial intelligence.
Session
No
Topics to be covered
Time
Ref
Teaching
Aids
50m
2,3,8
BB/ICT
50m
2,3,8
BB
31
Introduction to Knowledge Representation:
Reasoning, Issues and Acquisition Prepositional Calculus
Predicate Calculus
32
Rule Based knowledge Representation
50m
2,3,8
BB
33
Symbolic Reasoning Under Uncertainity
50m
2,3,8
BB
34
Basic knowledge Representation Issues
50m
2,3,8
BB
35
Knowledge acquisition
50m
2,3,8
BB
90m
-
-
36
CAT-II
Heuristic Search: Techniques for Heuristic search
50m
2,3,8
BB
37
State Space Search: Strategies
50m
2,3,8
BB
38
Implementation of Graph Search
50m
2,3,8
BB
39
Search based on Recursion;
50m
2,3,8
BB
40
Pattern directed Search
50m
2,3,8
BB
41
Production Systems
50m
2,3,8
BB
42
Learning. Revision
50m
2,3,8
BB
30
DOC/LP/01/28.02.02
LESSON PLAN
LP – CS 2053
LP Rev. No: 00
Sub Code & Name: CS 2053 – SOFT COMPUTING
Unit: V
Branch: EC
Date: 30/06/2014
Page 05 of 06
Semester: VII
UNIT V APPLICATIONS OF COMPUTATIONAL INTELLIGENCE
8
Syllabus:
Printed Character Recognition – Inverse Kinematics Problems – Automobile Fuel Efficiency
Prediction – Soft Computing for Color Recipe Prediction
.
Objective:
To gain more knowledge in machine learning through various applications.
Session
No
Topics to be covered
Time
Ref
Teaching
Aids
43
Printed Character Recognition
50m
7,8
BB
44
Printed Character Recognition (Cont.,)
50m
7,8
BB
45
Inverse Kinematics Problems
50m
7,8
BB
46
Inverse Kinematics Problems (Cont.,)
50m
7,8
BB
47
Automobile Fuel Efficiency Prediction
50m
7,8
BB
48
Automobile Fuel Efficiency Prediction (Cont.,)
50m
7,8
BB
49
Soft Computing for Color Recipe Prediction
50m
7,8
BB
50
Soft Computing for Color Recipe Prediction
(Cont.,). Revision
CAT-III
50m
7,8
BB
90m
-
-
DOC/LP/01/28.02.02
LESSON PLAN
LP – CS 2053
LP Rev. No: 00
Sub Code & Name: CS 2053 – SOFT COMPUTING
Date: 30/06/2014
Semester: VII Page 06 of 06
Branch: EC
Course Delivery Plan:
Week
UNIT
1
2
3
4
5
I II I II I II I II I II
6
7
I II
I II
1 1 1 1 1 1 2 2 2 2 4 4 4
8
4
CAT I
9
10
11
12
13
I II I II I II I II I II I II
4 3 3 3 3 3 3 3 5 5 5 5
CAT II
CAT III
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.
Approved by
Prepared by
Signature
Name
Designation
Date
Dr. S.Ganesh Vaidyanathan
&
S.P.Sivagnana Subramanian
HoD-EC & AP-EC
02.07.2014
Dr. S.Ganesh Vaidyanathan,
HoD-EC
02.07.2014