CS2053 SOFT COMPUTING SYLLABUS | ANNA UNIVERSITY BE ECE 7TH SEMESTER SYLLABUS REGULATION 2008 2011-2012

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CS2053 SOFT COMPUTING SYLLABUS | ANNA UNIVERSITY BE ECE 7TH SEMESTER SYLLABUS REGULATION 2008 2011-2012 BELOW IS THE ANNA UNIVERSITY SEVENTH SEMESTER BE ELECTRONICS AND COMMUNICATION ENGINEERING DEPARTMENT SYLLABUS IT IS APPLICABLE FOR ALL STUDENTS ADMITTED IN THE YEAR 2011-2012 (ANNA UNIVERSITY CHENNAI,TRICHY,MADURAI,TIRUNELVELI,COIMBATORE), 2008 REGULATION OF ANNA UNIVERSITY CHENNAI AND STUDENTS ADMITTED IN ANNA UNIVERSITY CHENNAI DURING 2009

CS2053 SOFT COMPUTING L T P C
3 0 0 3
94
UNIT I FUZZY SET THEORY 10
Introduction to Neuro – Fuzzy and Soft Computing – Fuzzy Sets – Basic Definition and
Terminology – Set-theoretic Operations – Member Function Formulation and
Parameterization – Fuzzy Rules and Fuzzy Reasoning – Extension Principle and Fuzzy
Relations – Fuzzy If-Then Rules – Fuzzy Reasoning – Fuzzy Inference Systems –
Mamdani Fuzzy Models – Sugeno Fuzzy Models – Tsukamoto Fuzzy Models – Input
Space Partitioning and Fuzzy Modeling.
UNIT II OPTIMIZATION 8
Derivative-based Optimization – Descent Methods – The Method of Steepest Descent –
Classical Newton’s Method – Step Size Determination – Derivative-free Optimization –
Genetic Algorithms – Simulated Annealing – Random Search – Downhill Simplex
Search.
UNIT III ARTIFICIAL INTELLIGENCE 10
Introduction, Knowledge Representation – Reasoning, Issues and Acquisition:
Prepositional and Predicate Calculus Rule Based knowledge Representation Symbolic
Reasoning Under Uncertainity Basic knowledge Representation Issues Knowledge
acquisition – Heuristic Search: Techniques for Heuristic search Heuristic Classification -
State Space Search: Strategies Implementation of Graph Search Search based on
Recursion Patent-directed Search Production System and Learning.
UNIT IV NEURO FUZZY MODELING 9
Adaptive Neuro-Fuzzy Inference Systems – Architecture – Hybrid Learning Algorithm –
Learning Methods that Cross-fertilize ANFIS and RBFN – Coactive Neuro Fuzzy
Modeling – Framework Neuron Functions for Adaptive Networks – Neuro Fuzzy
Spectrum.
UNIT V APPLICATIONS OF COMPUTATIONAL INTELLIGENCE 8
Printed Character Recognition – Inverse Kinematics Problems – Automobile Fuel
Efficiency Prediction – Soft Computing for Color Recipe Prediction.
TOTAL: 45 PERIODS
TEXT BOOKS
1. J.S.R.Jang, C.T.Sun and E.Mizutani, “Neuro-Fuzzy and Soft Computing”, PHI, 2004,
Pearson Education 2004.
2. N.P.Padhy, “Artificial Intelligence and Intelligent Systems”, Oxford University Press,
2006.
REFERENCES
1. Elaine Rich & Kevin Knight, Artificial Intelligence, Second Edition, Tata Mcgraw Hill
Publishing Comp., 2006, New Delhi.
2. Timothy J.Ross, “Fuzzy Logic with Engineering Applications”, McGraw-Hill, 1997.
3. Davis E.Goldberg, “Genetic Algorithms: Search, Optimization and Machine
Learning”, Addison Wesley, N.Y., 1989.
4. S. Rajasekaran and G.A.V.Pai, “Neural Networks, Fuzzy Logic and Genetic
Algorithms”, PHI, 2003.
5. R.Eberhart, P.Simpson and R.Dobbins, “Computational Intelligence - PC Tools”, AP
Professional, Boston, 1996.
6. Amit Konar, “Artificial Intelligence and Soft Computing Behaviour and Cognitive
model of the human brain”, CRC Press, 2008.