Syllabus
Unit 1
Brief review – Pitfalls of Traditional AI – Why computational intelligence? – Computational Intelligence concept, Neural Networks – single layer and multilayer, Backpropagation, Radial-Basis Function Networks, Recurrent Neural Networks.
Unit 2
Fuzzy sets, properties, membership function, fuzzy operations. Fuzzy logic and fuzzy inference and applications.
Evolutionary computation – constituent algorithms, Collective Intelligence – Swarm intelligence algorithms – Overview of other bio-inspired algorithms
Unit 3
Hybrid approaches (neural networks, fuzzy logics, genetic algorithm, etc) – Applications of Computational intelligence in Industrial applications, manufacturing and logistics – Fuzzy systems and Evolutionary algorithms.
Objectives and Outcomes
Course Objectives
- This course gives importance to make the students to understand the concepts of different computational methodologies to bring computational intelligence.
- This course covers learning the basics of Neural Network, Fuzzy Logic and Evolutionary Algorithms.
- This course also enables the student design and implement simple algorithms with Neural Network, Fuzzy Logic and Evolutionary Algorithms.
Course Outcomes
CO1: Understand the nature and purpose of different computational intelligent components.
CO2: Apply neural networks and applications in real-world scenarios.
CO3: Understand fuzzy systems in application scenarios.
CO4: Analyze the working of Evolutionary algorithms in optimization problems.
CO5: Apply Evolutionary approaches to application scenarios.
CO-PO Mapping
|
PO/PSO
|
PO1
|
PO2
|
PO3
|
PO4
|
PO5
|
PO6
|
PO7
|
PO8
|
PO9
|
PO10
|
PO11
|
PO12
|
PSO1
|
PSO2
|
|
CO
|
|
CO1
|
3
|
2
|
2
|
2
|
1
|
1
|
1
|
–
|
–
|
–
|
–
|
–
|
3
|
2
|
|
CO2
|
3
|
2
|
2
|
2
|
2
|
2
|
2
|
–
|
–
|
–
|
–
|
–
|
3
|
2
|
|
CO3
|
3
|
2
|
2
|
2
|
1
|
2
|
2
|
–
|
–
|
–
|
–
|
–
|
3
|
2
|
|
CO4
|
3
|
1
|
2
|
2
|
3
|
1
|
1
|
–
|
–
|
–
|
–
|
–
|
3
|
2
|
|
CO5
|
3
|
1
|
2
|
2
|
3
|
2
|
2
|
–
|
–
|
–
|
–
|
–
|
3
|
2
|
Evaluation Pattern
Evaluation Pattern: 70:30
|
Assessment
|
Internal
|
End Semester
|
|
Midterm
|
20
|
|
|
Continuous Assessment – Theory (*CAT)
|
10
|
|
|
Continuous Assessment – Lab (*CAL)
|
40
|
|
|
**End Semester
|
|
30 (50 Marks; 2 hours exam)
|
*CAT – Can be Quizzes, Assignments, and Reports
*CAL – Can be Lab Assessments, Project, and Report
**End Semester can be theory examination/ lab-based examination/ project presentation
Text Books / References
Textbook(s)
David B Fogel, Derong Liu, James M Keller. “Fundamentals of Computational Intelligence: Neural Networks, Fuzzy Systems, and Evolutionary Computation”. John Wiley & Sons; 2016
Konar A, ‘Computational Intelligence: Principles, Techniques and Applications”, Springer Verlat, 2005.
Reference(s)
Siddique, Nazmul, and Hojjat Adeli. “Computational intelligence: synergies of fuzzy logic, neural networks and evolutionary computing”. John Wiley & Sons, 2013.
Lam, Hak-Keung, and Hung T. Nguyen, eds. “Computational intelligence and its applications: evolutionary computation, fuzzy logic, neural network and support vector machine techniques”. World Scientific, 2012.
Eberhart RC, Shi Y. “Computational intelligence: concepts to implementations”. Elsevier; 2007
Karray F, Karray FO, De Silva CW. “Soft computing and intelligent systems design: theory, tools, and applications. Pearson Education”, First Edition, Pearson India, 2009.
Engelbrecht AP. “Computational intelligence: an introduction”. John Wiley & Sons; 2007.
