Sunday, July 8, 2012

CS9036-SOFT COMPUTING-B.E -CSE-COMPUTER SCIENCE AND ENGINEERING SEVENTH-VII SEMESTER 2008 REGULATION ANNA UNIVERSITY SYLLABUS



CSE Computer Science And Engineering VII-Seventh Semester Syllabus 2008 Regulation Anna University


AIM:
To give an overall understanding on the theories that are available to solve hard real- world problems

OBJECTIVES:
·    To  give  the  students  an  overall  knowledge  of  soft  computing  theories  and fundamentals
·    To give an understanding on the fundamentals of non-traditional technologies and approaches to solving hard real-world problems
·    Fundamentals of artificial neural networks, fuzzy sets and fuzzy logic and genetic algorithms.
·    Use of ANN, Fuzzy sets to solve hard real-world problems
·    To given an overview of Genetic algorithms and machine learning techniques to solving hard real-world problems
·    To study about the applications of these areas

UNIT I           INTRODUCTION                                                                                         9
Evolution of Computing - Soft Computing Constituents – From Conventional AI to Computational Intelligence Neural Networks - Scop and Evolution Models of Neural Networks Feed forward Networks Supervised Learning Neural Networks Associative memory networks Unsupervised learning networks Special Networks

UNIT II          FUZZY SETS AND FUZZY LOGIC                                                             9
Fuzzy Sets Operations on Fuzzy Sets Fuzzy Relations - Fuzzy Rules Non interactive   fuzzy   set –   Fuzzification–   Intuitio  inference Rank   orderin Defuzzification Max-membership principle, centroid method, center of sums, center of largest area.

UNIT III         FUZZY MEASURES AND REASONING                                                    9
Fuzzy arithmetic and measures Fuzzy reasoning approximate reasoning – categorical, qualitative, syllogistic, dispositional Fuzzy inference systems fuzzy decision making individual, multiperson, multi objective, Bayesian fuzzy logic control system architecture, model and application

UNIT IV        MACHINE LEARNING AND GENETIC ALGORITHM                                9
Machine Learning Techniques Machine Learning Using Neural Nets Genetic Algorithms (GA) –  Simple and General GA Classification of  Genetic Algorithm Messy, Adaptive, Hybrid, Parallel Holland Classifier System

UNIT V         APPLICATION AND IMPLEMENTATION SOFT COMPUTING                 9
Genetic algorithms -. Traveling Salesperson Problem, Internet Search Techniques – Fuzzy Controllers Bayesian Belief networks for Rocket Engine Contro - Neural Network, Genetic algorithm and Fuzzy logic implementation in C++ and Matlab




TEXT BOOK:

TOTAL: 45 PERIODS

1.    S.N. Sivanandam and S.N. Deepa, Principles of Soft Computing, Wiley India Ltd., First Indian Edition, 2007

REFERENCES:
1.    Jyh-Shing  Roger  Jang,  Chuen-Tsai  Sun,  Eiji  Mizutani,  “Neuro-Fuzzy  and  Soft
Computing, Prentice-Hall of India, 2003.
2.    James  A.  Freema an Davi M.  Skapura,  “Neural  Networks  Algorithms, Applications, and Programming Techniques, Pearson Edn., 2003.
3.    Georg J.   Klir  an B Yuan,  Fuzzy  Sets  an Fuzzy  Logic-Theor and
Applications, Prentice Hall, 1995.
4.    Amit Konar, Artificial Intelligence and Soft Computing, First Edition,CRC Press,
2000.
5.    Simon Haykin, “Neural Networks: A Comprehensive Foundation, Second Edition
Prentice Hall, 1999.
6.    Mitchell Melanie, An Introduction to Genetic Algorithm”, Prentice Hall, 1998.
7.    David  E.  Goldberg,  Genetic  Algorithms  in  Search,  Optimization  and  Machine
Learning, Addison Wesley, 1997.


7/08/2012 02:14:00 AM

0 comments:

Post a Comment

Related Posts Plugin for WordPress, Blogger...