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. 1. To develop MATLAB programming skill
    2. To implement the algorithms to optimize convex and non-convex optimization problems.
    3. To implement training algorithm for ANN
    4. To design fuzzy controller

Course Outcomes

At the end of the course, students will be able to
1. Solve convex and non-convex optimization problems
2. Use ANN for input-output mapping and classification problems
3. Understand dimension reduction techniques using PCA
4. Design fuzzy controller

Essential Reading

  1. S. S. Rao, Engineering Optimization: Theory and Practice, John Wiley & Sons
  2. S. Haykin, Neural Networks: A Comprehensive Foundation, Pearson

Supplementary Reading

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