Course

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.

• 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.

• 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

• 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.

• 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.

Evaluation Pattern: 70:30

*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