Syllabus
Computational intelligence (CI): Adaptation, Self-organization and Evolution, Biological and artificial neuron, Neural Networks Concepts, Paradigms, Implementations, Evolutionary computing: Concepts, Paradigms, Implementation, Swarm Intelligence, Artificial Immune Systems, Fuzzy systems: Concepts, Paradigms, Implementation, Hybrid systems, CI application: case studies may include sensor networks, digital systems, control, forecasting and time-series predictions.
Objectives and Outcomes
Learning Objectives
LO1 To introduce the principles of Computational Intelligence technique.
LO2 To provide insights on the various CI paradigms
LO3 To impart knowledge to select a suitable CI principle to solve engineering or real-life
problems.
Course Outcomes
CO1 Ability to understand concepts of basic principles of Computational Intelligence
techniques.
CO2 Ability to Understand various neural network architectures
CO3 Ability to analyse and define various fuzzy systems
CO4 Ability to design and implement suitable CI principle to solve engineering or real life.
CO-PO Mapping
| CO/PO |
PO1 |
PO2 |
PO3 |
PO4 |
PO5 |
| CO1 |
3 |
2 |
– |
– |
1 |
| CO2 |
3 |
1 |
2 |
– |
2 |
| CO3 |
3 |
2 |
2 |
– |
2 |
| CO4 |
3 |
3 |
3 |
– |
3 |
Text Books / References
Textbooks/References:
- C. Eberhart, “Computational Intelligence: Concept to Implementations”, Morgan
Kaufmann Publishers, 2007.
- A Konar, “Computational Intelligence: Principles, Techniques and Applications”,
Springer Verlag, 2005.
