Course Details
Subject {L-T-P / C} : EE4704 : Soft Computing Laboratory { 0-0-2 / 1}
Subject Nature : Practical
Coordinator : Prof. Ananyo Sengupta
Syllabus
1. Introduction to MATLAB programming
2. Convex optimization by Steepest Descend method
3. Convex optimization by Newton’s Method
4. Non-convex optimization by Genetic Algorithm
5. Non-convex optimization by Particle Swarm Optimization
6. Training of simple perceptron by Widro-Hopf solution and Gauss-Newton method
7. Linear classification by simple perceptron
8. Training Multi-layer perceptron by Back-propagation algorithm
9. Principal Component Analysis and its Application
10. Fuzzy Modelling and Inference by Fuzzy Rule Base
Course Objectives
- 1. To develop MATLAB programming skill <br />2. To implement the algorithms to optimize convex and non-convex optimization problems. <br />3. To implement training algorithm for ANN <br />4. To design fuzzy controller
Course Outcomes
At the end of the course, students will be able to <br />1. Solve convex and non-convex optimization problems <br />2. Use ANN for input-output mapping and classification problems <br />3. Understand dimension reduction techniques using PCA <br />4. Design fuzzy controller
Essential Reading
- S. S. Rao, Engineering Optimization: Theory and Practice, John Wiley & Sons
- S. Haykin, Neural Networks: A Comprehensive Foundation, Pearson
Supplementary Reading
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