COURSE OBJECTIVES & OUTCOMES 

Course Objectives:

• The course helps in understanding the concepts in Soft Computing techniques, Fuzzy systems, Genetic algorithms, Simulated annealing, Ant Colony Optimization, and Artificial Neural Networks, to apply these tools in solving problems, to analyze the strengths and weaknesses of these methods, and to choose appropriate Soft Computing techniques for a given problem. 

Course outcome:

• Explain concepts in Fuzzy sets, Fuzzy Logic, Genetic Algorithm, Simulated Annealing and Ant Colony Optimization.

•  Illustrate how Fuzzy Logic, Simulated annealing, Genetic Algorithm, and Ant Colony optimization are used to solve problems. 

•  Explain concepts in Artificial Neural Networks (MLP, RBFN, KSOM, ART, BAM, ELM, Deep NN, CNN, RNN). 

•  Illustrate the use of ANN in solving problems. 

•  Select an appropriate Soft Computing technique to solve a problem. 

•  Solve Engineering problems using Soft Computing techniques. 

EVALUATION COMPONENTS

COURSE WORK (100 MARKS)

1. Assignments (20%)  

                 a). Assignments, Quizzes, Class Participation, Course Projects.

2. Mid Sem Examination (30%)        

3. End Sem Examination (50%)

LECTURE NOTES