
	Courseware : Soft Computing - Course Content, Lecture note, Slides, Text books, References


	last updated on Aug. 10, 2010

The Course on Soft Computing refers to the odd semester (July–Nov) course, title Soft Computing, Code 07B71CI4-0-8, 4 Credits, Lectures – 42 hours, offered to the students of 7th semester B.Tech course in the year, 2007, 2008, 2009 and 2010. The lecture slides, 398 numbers in pdf format, have gone through three updates. This course is at the Dept. of Computer Science & Engineering, Jaypee University of Engineering and Technology (JUET), Guna. This course is offered by Prof. RC Chakraborty, Visiting Professor at JUET.


	B.Tech Course on Soft Computing, 42 hrs, Topics 9, Slides 398 in pdf format.

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	Content	Hrs
00	Soft Computing : Course Content
01	Introduction to Soft Computing : Introduction, Fuzzy Computing, Neural Computing, Genetic Algorithms, Associative Memory, Adaptive Resonance Theory, Applications.	1-6
02	Fundamentals of Neural Network : Introduction, Model of Artificial Neuron, Architectures, Learning Methods, Taxonomy of NN Systems, Single-Layer NN System, Applications.	7-14
03	Back Propagation Network : Background, Back-Propagation Learning, Back-Propagation Algorithm.	15-20
04	Associative Memory : Description, Auto-associative Memory, Bi-directional Hetero-associative Memory.	21-24
05	Adaptive Resonance Theory : Recap - supervised, unsupervised, backprop algorithms; Competitive Learning; Stability-Plasticity Dilemma (SPD), ART Networks, Iterative Clustering, Unsupervised ART Clustering.	25-28
06	Fuzzy Set Theory : Introduction, Fuzzy set : Membership, Operations, Properties; Fuzzy Relations.	29-34
07	Fuzzy Systems : Introduction, Fuzzy Logic, Fuzzification, Fuzzy Inference, Fuzzy Rule Based System, Defuzzification	35-36
08	Fundamentals of Genetic Algorithms : Introduction, Encoding, Operators of Genetic Algorithm, Basic Genetic Algorithm.	37-40
09	Hybrid Systems : Integration of Neural Networks, Fuzzy Logic and Genetic Algorithms, GA Based Back Propagation Networks, Fuzzy Back Propagation Networks, Fuzzy Associative Memories, Simplified Fuzzy ARTMAP.	41-42

Acknowledgments

In the preparation of the course material, any quote, paraphrase or summary, information, idea, text, data, table, figure or any other material which originally appeared in someone else’s work, I sincerely acknowledge them.

Recommended Textbooks
1	"Neural Network, Fuzzy Logic, and Genetic Algorithms - Synthesis and Applications", by S. Rajasekaran and G.A. Vijayalaksmi Pai, (2005), Prentice Hall, Chapter 1-15, page 1-435.
2	“Soft Computing and Intelligent Systems - Theory and Application”, by Naresh K. Sinha and Madan M. Gupta (2000), Academic Press, Chapter 1-25, page 1-625.
3	"Soft Computing and Intelligent Systems Design - Theory, Tools and Applications", by Fakhreddine karray and Clarence de Silva (2004), Addison Wesley, chapter 1-10, page 1-533.
4	“Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence” by J. S. R. Jang, C. T. Sun, and E. Mizutani, (1996), Prentice Hall, Chapter 1-15, page 1-607.
5	"Soft Computing : Integrating Evolutionary, Neural, and Fuzzy Systems", by Tettamanzi, Andrea, Tomassini, and Marco. (2001), Springer, Chapter 1-9, page 1-323.
6	"Neural Networks: A Comprehensive Foundation", by Simon S. Haykin, (1999), Prentice Hall, Chapter 1-15, page 1-889.
7	"Elements of Artificial Neural Networks", by Kishan Mehrotra, Chilukuri K. Mohan and Sanjay Ranka, (1996), MIT Press, Chapter 1-7, page 1-339.
8	"Fundamentals of Neural Networks: Architecture, Algorithms and Applications", by Laurene V. Fausett, (1993), Prentice Hall, Chapter1-7, page 1-449.
9	"Neural Network Design", by Martin T. Hagan, Howard B. Demuth and Mark Hudson Beale, ( 1996) , PWS Publ. Company, Chapter 1-19, page 1-1 to 19-14.
10	"An Introduction to Neural Networks", by James A. Anderson, (1997), MIT Press, Chapter 1- 17, page 1-585.
11	"Fuzzy Sets and Fuzzy Logic: Theory and Applications", by George J. Klir and Bo Yuan, (1995), Prentice Hall, Chapter 1-17, page 1-574.
12	"Introduction To Fuzzy Sets And Fuzzy Logic", by M Ganesh, (2008), Prentice-hall, Chapter 1-10, page 1- 256.
13	"Fuzzy Logic: Intelligence, Control, and Information", by John Yen, Reza Langari, (1999 ), Prentice Hall, Chapter 1-17, page 1-543.
14	"Fuzzy Logic with Engineering Applications", by Timothy Ross, (2004), John Wiley & Sons Inc, Chapter 1-15 , page 1-623.
15	"Fuzzy Logic and Neuro Fuzzy Applications Explained", by Constantin Von Altrock, (1995), Prentice Hall, Chapter 1-8, page 1-368.
16	"Genetic Algorithms in Search, Optimization, and Machine Learning", by David E. Goldberg, (1989), Addison-Wesley, Chapter 1-8, page 1- 432.
17	"An Introduction to Genetic Algorithms", by Melanie Mitchell, (1998), MIT Press, Chapter 1- 6, page 1- 203,
18	"Genetic Algorithms: Concepts And Designs", by K. F. Man, K. S. and Tang, S. Kwong, (201), Springer, Chapter 1- 10, page 1- 348,
19	"Genetic algorithms and engineering design", by Mitsuo Gen, and Runwei Cheng, (1997), John Wiley & Sons Inc, chapter 1- 10, page 1-411.
20	"Practical genetic algorithms", by Randy L. Haupt, (2004), John Wiley & Sons Inc, Chapter 1- 7, page 1- 251.
21	Related documents from open source, mainly internet. An exhaustive list is being prepared for inclusion at a later date.

References
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last updated on Aug. 10, 2010

B.Tech Course on Soft Computing, 42 hrs, Topics 9, Slides 398 in pdf format.

Content

00

Soft Computing :

Course Content

01

Introduction to Soft Computing :

Introduction, Fuzzy Computing, Neural Computing, Genetic Algorithms, Associative Memory, Adaptive Resonance Theory, Applications.

02

Fundamentals of Neural Network :

Introduction, Model of Artificial Neuron, Architectures, Learning Methods, Taxonomy of NN Systems, Single-Layer NN System, Applications.

03

Back Propagation Network :

Background, Back-Propagation Learning, Back-Propagation Algorithm.

04

Associative Memory :

Description, Auto-associative Memory, Bi-directional Hetero-associative Memory.

05

Adaptive Resonance Theory :

Recap - supervised, unsupervised, backprop algorithms; Competitive Learning; Stability-Plasticity Dilemma (SPD), ART Networks, Iterative Clustering, Unsupervised ART Clustering.

06

Fuzzy Set Theory :

Introduction, Fuzzy set : Membership, Operations, Properties; Fuzzy Relations.

07

Fuzzy Systems :

Introduction, Fuzzy Logic, Fuzzification, Fuzzy Inference, Fuzzy Rule Based System, Defuzzification

08

Fundamentals of Genetic Algorithms :

Introduction, Encoding, Operators of Genetic Algorithm, Basic Genetic Algorithm.

09

Hybrid Systems :

Integration of Neural Networks, Fuzzy Logic and Genetic Algorithms, GA Based Back Propagation Networks, Fuzzy Back Propagation Networks, Fuzzy Associative Memories, Simplified Fuzzy ARTMAP.