Unit I syllabus: INTRODUCTION TO SOFT COMPUTING AND NEURAL NETWORKS 9

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DOC/LP/01/28.02.02



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
C
omputing
(SC)

50

1,2,3,5,8

BB

2

Evolution of Computing

50

1,2,3,5,8

BB

3

Soft Computing Constituents


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LESSON PLAN

LP
-

CP 9254

LP Rev. No: 00

Date:
12
/0
2
/201
2

Page 01 of 06


Sub Code & Name:

CP 9254
-

SOFT COMPUTING


Unit:

I


Branch:
M
.E.(
C
.
S
.)



Semester:
II

DOC/LP/01/28.02.02




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 kno
wledge 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

1
4,15

Applica
tion 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

17
,18

Machine Learning Approach to Knowledge
Acquisition.

50

5

BB


CAT
-
II

40












LESSON PLAN

LP
-

CP 9254

LP Rev. No: 00

Date: 12/02/2012

Page 02 of 06


Sub Code & Name:

CP 9254
-

SOFT COMPUTING


Unit:

II

Branch:
M.E.(C.S.)



Semester:
II

DOC/LP/01/28.02.02




Unit


II
I syllabus:

NEURAL NETWORKS








9



Machine Learning Using Neural Net
work, 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.


Objec
tive:


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

22
,23

Supervised Learning Neural Networks

50

1,3,8

BB

2
4

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

30

Advances in Neural networks

50

1,3,8

BB


CAT
-
III

40




LESSON PLAN

LP
-

CP 9254

LP Rev. No
: 00

Date: 12/02/2012

Page 03 of 06


Sub Code & Name:

CP 9254
-

SOFT COMPUTING


Unit:

III

Branch:
M.E.(C.S.)


Semester:
II

DOC/LP/01/28.02.02



Unit


I
V

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









LESSON PLAN

LP
-

CP 9254

LP Rev. No: 00

Date: 12/02/2012

Page 04 of 06


Sub Code & Name:

CP 9254
-

SOFT COMPUTING


Unit:

IV


Branch:
M.E.(C.S.)


Semester:
II

Session

No

Topics to be covered

Time
Allocation

(min)

Books
Referred

Teaching
Method

31

Fuzzy Sets

50

2

BB

32

Operations on Fu
zzy 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


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
vario
us stages in Neuro
-
Fuzzy Modeling


Session

No

Topics to be covered

Time
Allocation

(min)

Books
Referred

Teaching
Method

40

Adaptive Neuro
-
Fuzzy Inference Systems


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LESSON PLAN

LP
-

CP 9254

LP Rev. No: 00

Date: 12/02/2012

Page 05 of 06


Sub Code & Name:

CP 9254
-

SOFT COMPUTING


Unit:

V

Branch:
M.E.(C.S.)


Semester:
II

DOC/LP/01/28.02.02




Course Delivery Plan:



Week

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

I II

I II

I II

I II

I II

I II

I II

I II

I II

I II

I II

I II

I II

I II

I II


UNIT


1


1

1

1

1

C

A

T

1

2

2

2

2

2

C

A

T

2

3

3

3

3

3

C

A

T

3

4

4

4

4

4

C

A

T

4

5

5

5

5

5

C

A

T

5


TEXT BOOKS:


1. Jyh
-
Shing Roger Jang, Chuen
-
Tsai Sun, Eiji Mizutani, “Neuro
-
Fuzzy and Soft

Computing”, Prentice
-
Hall of Indi
a, 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

Signatur
e



Name

S.P.Sivagnana Subramanian

Prof. E.G.Govindan

Designation

Assistant Professor
-

EC

Vice Principal & HOD
-
EC

Date





LESSON PLAN

LP
-

CP 9254

LP Rev. No: 00

Date: 12/02/2012

Page 06 of 06


Sub Code & Name
:

CP 9254
-

SOFT COMPUTING


Branch:
M.E.(C.S.)





Semester:
II