Course Details

Subject {L-T-P / C} : EC4701 : Soft Computing Laboratory { 0-0-2 / 1}

Subject Nature : Practical

Coordinator : Prof. Samit Ari

Syllabus

1. Implementation of a 2 input AND and OR logic function using perceptron. Start with different set of initial weights and show that there is more than one solution to the problem.
2. Develop an algorithm using Hebbian Learning rule to solve NAND and NOR problem. Assume the elements of initial weight matrix as random value and study the effect of different learning rates.
3. Solve the following Boolean function using Perceptron learning rule:
4. Consider the following function:

Perform two iterations of Newton’s method from the initial guess x0 = [1, -1]T.
5. Demonstrate that EX-OR and XNOR gate is a non-linearly separable problem. Design a MLP for the purpose and train it using BP algorithm. Assume the use of a logistic function for the nonlinearity.
6. Using a MLP with BP algorithm approximate the function y=1+Sin(2px) for -1=x=1. Draw the actual output and simulated output. Show the signal matching after 100/ 500/ 1000 iterations.
7. Implement a 2input EX-OR gate using regularized RBF (use 4 centres). Plot the output surface for different input variables. Extend this for a 3 input RBF using 8 centers. Tabulate the input output pattern mapping for each case.
8. Using MLP/ RBF design a non-stationary time series prediction network to predict the output against time sample.
9. Let the meanings of the linguistic terms young and old be defined by the following membership function:
, .
Plot the MFs for the following linguistic values using MATLAB:
• More or less old
• Not young and not old
• Young but not too young
• Extremely old
10. Mini Project

Course Objectives

Course Outcomes

Students will able to learn how to develop the different Soft Computing techniques, e.g. neural networks, fuzzy logic using MATLAB/C platform for real time application.

Essential Reading

Supplementary Reading