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Course Detail

Course Name Computational Intelligence
Course Code 15CSE338
Program B. Tech. in Computer Science and Engineering
Year Taught 2019


Unit 1

Artificial Intelligence – a brief review – Pitfalls of traditional AI – Why Computational Intelligence? – Computational intelligence concept – Importance of tolerance of imprecision and uncertainty – Constituent techniques – Overview of Artificial Neural Networks, Fuzzy Logic, Evolutionary Computation.

Unit 2

Neural Network: Biological and artificial neuron, neural networks, supervised and unsupervised learning. Single layer Perceptron, Multilayer Perceptron – Back propagation learning. Neural networks as associative memories – Hopfield networks, Bidirectional Associative Memory. Topologically organized neural networks – competitive learning, kohonen maps.

Unit 3

Fuzzy Logic: Fuzzy sets, properties, membership functions, fuzzy operations. Fuzzy logic and fuzzy inference and applications, Evolutionary Computation – constituent algorithms, Swarm intelligence algorithms – Overview of other bio-inspired algorithms – Hybrid approaches (neural networks, fuzzy logic, genetic algorithms etc.)

Text Books

  • Kumar S., “Neural Networks – A Classroom Approach”, Tata McGraw Hill, 2004.
  • Konar A., “Computational Intelligence: Principles, Techniques and Applications”, Springer Verlag, 2005


  • Engelbrecht, A.P, “Fundamentals of Computational Swarm Intelligence”, John Wiley & Sons, 2006.
  • Ross T J, “Fuzzy Logic with Engineering Applications”, McGraw Hill, 2002.
  • Eiben A E and Smith J E, “Introduction to Evolutionary Computing”, Second Edition, Springer, Natural Computing Series, 2007.
  • Jang J S R and Sun C T, Mizutani E, “Neuro – Fuzzy and Soft Computing”, PHI, 2002.
  • Rajashekaran S and Vijayalakshmi Pai G A, “Neural Networks, Fuzzy Logic and Genetic Algorithms”, PHI, 2003.

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