Proceedings of the 26th Academic Council held on 18.5.2012
CSE319 SOFT COMPUTING
L T P C 3 0 0 3
Version No: Course Prerequisites:
Discrete Mathematical Structures
Objectives: To introduce the concepts of neural networks and advanced neural networks To understand the fundamentals of fuzzy sets and fuzzy logic 1. To establish basic knowledge about optimization techniques in soft computing • •
Expected Outcomes: At the end of course student should be able to 2. Design soft computing techniques for various applications domains Lead project teams in the design of soft computing related projects •
Unit I Unit I NEURAL NETWORKS 8 hours History, Mathematical model of neuron, ANN architectures, Learning rules, Learning Paradigms. Perceptron network, Backpropagation network, Backpropagation learning and its applications, Variants of BPA. Unit II Unit II ADVANCED NEURAL NETWORKS 9 hours Associative Memory: Auto correlation, Hetero Correlation, Exponential BAM, Applications. Adaptive Resonance Theory: Vector Quantization, ART1, ART2, applications, Kohonen’s Self Organizing Map. Unit III Unit III FUZZY SETS FUZZY SETS AND AND RELATIONS 8 hours Uncertainty and Imprecision, Chance vs ambiguity, Fuzzy Sets, Fuzzy Relations, Membership functions, Properties of Membership functions, Fuzzification and Defuzzification. Unit IV Unit IV FUZZY LOGIC FUZZY LOGIC 10 hours Classical Logic and Fuzzy logic, Fuzzy Rule based systems, Fuzzy Decision making, Fuzzy Classification, Fuzzy Pattern Recognition, Applications – MATLAB and Soft Computing. Unit V Unit V OPTIMIZATION TECHNIQUES 10 hours Derivative Derivative based Optimizati Optimization on – Descent Descent Methods – Genetic Genetic Algorithms Algorithms – Ant Colony Optimization – Particle Swarm Optimization, Case Study - fraud detection, health care using Soft computing techniques. Text / Text / Reference Books 1. T. J. Ross, “Fuzzy Logic with Engineering Applications”, John Wiley & Sons, 3 rd Edition, 2010. 2. S.N Sivanandam, S N Deepa,” Principles of Soft Computing”, Wiley India, 2nd Edition, 2011. 3. S. Rajasekaran and G.A.V. Pai, “Neural Networks, Fuzzy Logic and Genetic Algorithms: Synthesis and Applications”, PHI, 2009.
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Proceedings of the 26th Academic Council held on 18.5.2012
4. 5.
Davis E.Goldberg, “Genetic Algorithms: Search, Optimization and Machine Learning”, Pearson Education, Fourth impression 2009. Zurada, J. M. “Introduction to Artificial Neural Systems”, Jaico Publishing House, 1997.
Mode of Evaluation
: Tests, Assignments, Seminars
Recommended by the Board of Studies on Date of Approval by the Academic Council
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