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Course Co-ordinated by IIT Kharagpur
Prof. Debasis Samanta
IIT Kharagpur


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Soft computing is an emerging approach to computing which parallel the remarkable ability of the human mind to reason and learn in an environment of uncertainty and imprecision. Soft computing is based on some biological inspired methodologies such as genetics, evolution, antís behaviors, particles swarming, human nervous systems, etc. Now, soft computing is the only solution when we donít have any mathematical modeling of problem solving (i.e., algorithm), need a solution to a complex problem in real time, easy to adapt with changed scenario and can be implemented with parallel computing. It has enormous applications in many application areas such as medical diagnosis, computer vision, hand written character recondition, pattern recognition, machine intelligence, weather forecasting, network optimization, VLSI design, etc.




Introduction to Soft Computing, Introduction to Fuzzy logic,Fuzzy membership functions,Operations on Fuzzy sets


Fuzzy relations, Fuzzy propositions, Fuzzy implications, Fuzzy inferences


Defuzzyfication Techniques-I, Defuzzyfication Techniques-II, Fuzzy logic controller-I, Fuzzy logic controller-II


Solving optimization problems, Concept of GA, GA Operators: Encoding,GA Operators: Selection-I


GA Operators: Selection-II, GA Operators: Crossover-I, GA Operators: Crossover-II, GA Operators: Mutation


Introduction to EC-I, Introduction to EC-II, MOEA Approaches: Non-Pareto, MOEA Approaches: Pareto-I


MOEA Approaches: Pareto-II, Introduction to ANN, ANN Architecture


ANN Training-I, ANN Training-II, ANN Training-III, Applications of ANN


1.  An Introduction to Genetic Algorithm  Melanic Mitchell (MIT Press)2. Evolutionary Algorithm for Solving Multi-objective, Optimization Problems (2nd Edition), Collelo, Lament, Veldhnizer ( Springer)

3. Fuzzy Logic with Engineering Applications Timothy J. Ross (Wiley)

4. Neural Networks and Learning Machines Simon Haykin (PHI)



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