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DOC/LP/01/28.02.02
LESSON PLAN
LP- CP 9254
LP Rev. No: 00
Sub Code & Name: CP 9254 - SOFT COMPUTING
Date: 12/02/2012
Unit: I
Branch: M.E.(C.S.)
Semester: II
Page 01 of 06
Unit – I syllabus: INTRODUCTION TO SOFT COMPUTING AND NEURAL NETWORKS
9
Evolution of Computing - Soft Computing Constituents – From Conventional AI to Computational
Intelligence - Machine Learning Basics
Objective:
To impart knowledge on origin and basics of soft computing and Neural Networks.
Session
No
Topics to be covered
Time
Allocation
(min)
Books
Referred
Teaching
Method
1
Overview on the course and Introduction to
Soft Computing(SC)
50
1,2,3,5,8
BB
2
Evolution of Computing
50
1,2,3,5,8
BB
3
Soft Computing Constituents – Historical
Sketch and traditional AI
50
1
BB
4
Soft Computing techniques and structure
50
1,2,3,5,8
BB
5
Comparative characteristics of Constituents of
SC
50
1
BB
6
Neuro – Fuzzy and SC characteristics
50
1
BB
7
From Conventional AI to
Computational Intelligence
50
1
BB
Machine Learning Basics
50
1,2,3,5,8
BB
CAT-I
40
8,9
DOC/LP/01/28.02.02
LESSON PLAN
LP- CP 9254
LP Rev. No: 00
Sub Code & Name: CP 9254 - SOFT COMPUTING
Date: 12/02/2012
Unit: II
Branch: M.E.(C.S.)
Semester: II
Page 02 of 06
Unit – II syllabus: GENETIC ALGORITHMS
9
Introduction to Genetic Algorithms (GA) – Applications of GA in Machine Learning – Machine
Learning Approach to Knowledge Acquisition.
Objective :
To impart knowledge on genetic algorithms and their applications.
Session
No
Topics to be covered
Time
Allocation
(min)
Books
Referred
Teaching
Method
10
Introduction to GA
50
5
BB
11
Mathematical foundation
50
5
BB
12,13
Implementation of GA
50
5
BB
14,15
Application of GA
50
5
BB
15
Introduction to genetic based machine learning
50
5
BB
16
Application of GA in machine learning
50
5
BB
Machine Learning Approach to Knowledge
Acquisition.
50
5
BB
CAT-II
40
17,18
DOC/LP/01/28.02.02
LESSON PLAN
LP- CP 9254
LP Rev. No: 00
Sub Code & Name: CP 9254 - SOFT COMPUTING
Date: 12/02/2012
Unit: III
Branch: M.E.(C.S.)
Semester: II
Page 03 of 06
Unit – III syllabus: NEURAL NETWORKS
9
Machine Learning Using Neural Network, Adaptive Networks – Feed forward Networks – Supervised
Learning Neural Networks – Radial Basis Function Networks - Reinforcement Learning – Unsupervised
Learning Neural Networks – Adaptive Resonance architectures – Advances in Neural networks.
Objective:
To impart knowledge on various types of neural networks, learning methods and their applications
Session
No
Topics to be covered
Time
Allocation
(min)
Books
Referred
Teaching
Method
19
Machine Learning Using Neural Network
50
1,3,8
BB
20
Adaptive Networks
50
1,3,8
BB
21
Feed forward Networks
50
1,3,8
BB
Supervised Learning Neural Networks
50
1,3,8
BB
24
Radial Basis Function Networks
50
1,3,8
BB
25
Reinforcement Learning
50
1,3,8
BB
26,27
Unsupervised Learning Neural Networks
50
1,3,8
BB
28,29
Adaptive Resonance architectures
50
1,3,8
BB
Advances in Neural networks
50
1,3,8
BB
CAT-III
40
22,23
30
DOC/LP/01/28.02.02
LESSON PLAN
LP- CP 9254
LP Rev. No: 00
Sub Code & Name: CP 9254 - SOFT COMPUTING
Date: 12/02/2012
Unit: IV
Branch: M.E.(C.S.)
Semester: II
Page 04 of 06
Unit – IV syllabus: FUZZY LOGIC
9
Fuzzy Sets – Operations on Fuzzy Sets – Fuzzy Relations – Membership Functions- Fuzzy Rules and
Fuzzy Reasoning – Fuzzy Inference Systems – Fuzzy Expert Systems – Fuzzy Decision Making
Objective:
To impart knowledge on fuzzy logic and different stages in fuzzy systems
Session
No
Topics to be covered
Time
Allocation
(min)
Books
Referred
Teaching
Method
31
Fuzzy Sets
50
2
BB
32
Operations on Fuzzy Sets
50
2
BB
33
Fuzzy Relations
50
2
BB
34
Membership Functions
50
2
BB
35
Fuzzy Rules
50
2
BB
36
Fuzzy Reasoning
50
2
BB
37
Fuzzy Inference Systems
50
2
BB
38
Fuzzy Expert Systems
50
2
BB
39
Fuzzy Decision Making
50
2
BB
CAT-IV
40
DOC/LP/01/28.02.02
LESSON PLAN
LP- CP 9254
LP Rev. No: 00
Sub Code & Name: CP 9254 - SOFT COMPUTING
Date: 12/02/2012
Unit: V
Branch: M.E.(C.S.)
Semester: II
Page 05 of 06
Unit – V syllabus: NEURO-FUZZY MODELING
9
Adaptive Neuro-Fuzzy Inference Systems – Coactive Neuro-Fuzzy Modeling – Classification and
Regression Trees – Data Clustering Algorithms – Rulebase Structure Identification – Neuro-Fuzzy
Control – Case studies
Objective :
To impart knowledge on various stages in Neuro-Fuzzy Modeling
Time
Allocation
(min)
Books
Referred
Teaching
Method
50
1
BB
50
1
BB
50
1
BB
Analysis of adaptive learning capability
50
1
BB
Classification and Regression Trees
50
1
BB
50
1
BB
50
1
BB
Neuro-Fuzzy Control – I & II and case studies
50
1
BB
Review
50
1-8
BB
CAT-V
40
Session
No
40
41
42
43
44,45
46,47
48
49.50
51
Topics to be covered
Adaptive Neuro-Fuzzy Inference Systems –
ANFIS architecture
Hybrid learning algorithm
Coactive Neuro-Fuzzy Modeling – Frame
work and neuron functions
Data Clustering Algorithms: K- means, Fuzzy
C-means, Mountain and subtractive clustering
Rulebase Structure Identification organization
DOC/LP/01/28.02.02
LESSON PLAN
LP- CP 9254
LP Rev. No: 00
Sub Code & Name: CP 9254 - SOFT COMPUTING
Date: 12/02/2012
Branch: M.E.(C.S.)
Semester: II
Page 06 of 06
Course Delivery Plan:
Week
UNIT
1
2
3
4
5
6
7
I II I II I II I II I II I II I II
C
C
A
A
1 1 1 1 1
2 2 2 2 2
3 3
T
T
1
2
8
9
10
11
12
13
14
15
I II I II I II I II I II I II I II I II
C
C
C
A
A
A
3 3 3
4 4 4 4 4
5 5 5 5 5
T
T
T
3
4
5
TEXT BOOKS:
1. Jyh-Shing Roger Jang, Chuen-Tsai Sun, Eiji Mizutani, “Neuro-Fuzzy and Soft Computing”, PrenticeHall of India, 2003
2. George J. Klir and Bo Yuan, “Fuzzy Sets and Fuzzy Logic-Theory and Applications”,Prentice Hall,
1995
3. James A. Freeman and David M. Skapura, “Neural Networks Algorithms, Applications, and
Programming Techniques”, Pearson Edn., 2003
REFERENCES:
4. Mitchell Melanie, “An Introduction to Genetic Algorithm”, Prentice Hall, 1998
5. David E. Goldberg, “Genetic Algorithms in Search, Optimization and Machine Learning”, Addison
Wesley, 1997
6. S. N. Sivanandam, S. Sumathi and S. N. Deepa, “Introduction to Fuzzy Logic using MATLAB”,
Springer, 2007.
7. S.N.Sivanandam · S.N.Deepa, “ Introduction to Genetic Algorithms”, Springer, 2007.
8. Jacek M. Zurada, “Introduction to Artificial Neural Systems”, PWS Publishers, 1992.
Prepared by
Approved by
S.P.Sivagnana Subramanian
Prof. E.G.Govindan
Assistant Professor - EC
Vice Principal & HOD-EC
Signature
Name
Designation
Date