Course info

Course Outcomes: 

At the end of this course, students will demonstrate the ability to: 

CO1: Illustrate soft computing techniques like neural networks and fuzzy logic and their roles in building intelligent systems. 

CO2: Illustrate and implement the various learning rules 

CO3: Comprehend the fuzzy logic and the concept of fuzziness involved in various systems and fuzzy set theory. 

CO4: Understand the concepts of fuzzy sets, knowledge representation using fuzzy rules, approximate reasoning, fuzzy inference systems, and fuzzy logic 

CO5: Design and Implement real-life examples using fuzzy logic and genetic algorithms.

List of Experiments:

The following experiments are to be demonstrated using any of the software tools like MATLAB, Python etc.

1. Write a program to implement the Perceptron Training Algorithm.

2. Write a program to Implement Hebb’s Rule

3. Write a program to Implement Delta Rule

4. Write a program to implement the Back-propagation algorithm

5. Write a program to implement a Hopfield Net

6. Write a program to implement a BAM

7. Write a program to Implement PCA

8. Write a program to Implement SVM

9. Write a program for pattern classification/pattern recognition

10. Write a program to study Fuzzy vs. crisp Logic.

11. Write a program to study and implement fuzzy set operations.

12. Write a program to illustrate the various fuzzy operations

13. Write a program to study and implement fuzzy relational operations.

14. Write a program to design and implement a fuzzy temperature controller.

15. Write a program to design and implement a Fuzzy Traffic light controller.

16. Write a program to study and implement the concept of Fuzzy C – means Clustering.

17. Write a program to implement Genetic Algorithms

18. Write a program to solve TSP (Travelling Salesman Problem) using a genetic algorithm.