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MC9296 SOFT COMPUTING Syllabus for 5th Sem MCA - Fifth semester - Regulation 2009 - Anna University

Written By Anonymous on Friday, November 2, 2012 | 11/02/2012


MC9296                                      SOFT COMPUTING                                             LT P C

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UNIT I           INTRODUCTION TO SOFT COMPUTING AND NEURAL NETWORKS   9

Evolution  of  Computing  -  Soft  Computing  Constituents  â€"  From  Conventional  AI  to

Computational Intelligence - Machine Learning Basics

UNIT II            GENETIC ALGORITHMS                                                                         9

Introduction to Genetic Algorithms (GA) â€" Applications of GA in Machine Learning - Machine Learning Approach to Knowledge Acquisition.

UNIT III           NEURAL NETWORKS                                                                              9

Machine Learning Using Neural Network, Adaptive Networks â€" Feed forward Networks â€" Supervised  Learning  Neural  Networks  â€"  Radial  Basis  Function  Networks  - Reinforcement Learning â€" Unsupervised Learning Neural Networks â€" Adaptive Resonance architectures â€" Advances in Neural networks.

UNIT IV          FUZZY LOGIC                                                                                           9

Fuzzy Sets â€" Operations on Fuzzy Sets â€" Fuzzy Relations â€" Membership Functions- FuzzyRules and Fuzzy Reasoning â€" Fuzzy Inference Systems â€"  Fuzzy Expert Systems

â€" Fuzzy Decision Making.

UNIT V           NEURO-FUZZY MODELING                                                                    9

Adaptive Neuro-Fuzzy Inference Systems â€" Coactive Neuro-Fuzzy Modeling â€" Classification and Regression Trees â€" Data Clustering Algorithms â€" Rulebase Structure Identification â€" Neuro-Fuzzy Control â€" Case studies.

TOTAL : 45 PERIODS


TEXT BOOKS:

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

Computing”, Prentice-Hall 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 Progra< span>mming Techniques”, Pearson Edn., 2003.

REFERENCES:

1.  Mitchell Melanie, “An Introduction to Genetic Algorithm”, Prentice Hall, 1998.

2.  David  E.  Goldberg,  “Genetic  Algorithms  in  Search,  Optimization  and  Machine

Learning”, Addison Wesley, 1997.

3.  S. N. Sivanandam, S. Sumathi and S. N. Deepa, “Introduction to Fuzzy Logic using

MATLAB”, Springer, 2007.

4.  S.N.Sivanandam · S.N.Deepa, “ Introduction to Genetic Algorithms”, Springer, 2007.

5.  Jacek M. Zurada, “Introduction to Artificial Neural Systems”, PWS Publishers, 1992.

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