Anna University Tiruchirappalli - 620 024
Regulations 2007 Sylllabus
M.E. COMPUTER SCIENCE AND ENGINEERING ELECTIVES
CS5002 – SOFT COMPUTING
UNIT I FUZZY SET THEORY 10
Regulations 2007 Sylllabus
M.E. COMPUTER SCIENCE AND ENGINEERING ELECTIVES
CS5002 – SOFT COMPUTING
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.
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.
Newton’s Method – Step Size Determination – Derivative Free Optimization – Genetic Algorithms – Simulated Annealing – Random Search – Downhill Simplex Search.
UNIT III NEURAL NETWORKS 10
Supervised Learning Neural Networks – Perceptrons – Adaline – Backpropagation Multilayer perceptrons – Radial Basis Function Networks – Unsupervised Learning and Other Neural
Networks – Competitive Learning Networks – Kohonen Self – Organizing Networks – Learning
Vector Quantization – Hebbian Learning.Supervised Learning Neural Networks – Perceptrons – Adaline – Backpropagation Multilayer perceptrons – Radial Basis Function Networks – Unsupervised Learning and Other Neural
Networks – Competitive Learning Networks – Kohonen Self – Organizing Networks – 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 APPLICATION OF COMPUTATIONAL INTELLIGENCE 8
Printed Character Recognition – Inverse Kinematics Problems – Automobile Fuel Efficiency
Prediction – Soft Computing for Color Recipe Prediction.
Total: 45
TEXTBOOK
1. J. S. R. Jang, C. T. Sun and E. Mizutani, “Neuro Fuzzy and Soft Computing”, PHI, Pearson
Education, 2004.
REFERENCES
1. Timothy J. Ross,”Fuzzy Logic with Engineering Application “, McGraw Hill, 1977.
2. Davis E. Goldberg,”Genetic Algorithms Search, Optimization and Machine Learning”,
Addison Wesley, 1989.
3. S. Rajasekaran and G. A. V. Pai, ”Neural Networks, Fuzzy Logic and Genetic Algorithms”,
PHI, 2003.
4. R. Eberhart, P. Simpson and R. Dobbins, ”Computational Intelligence PC Tools”, AP
Professional, Boston, 1996.
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