Subject Code: EE3L008  Subject Name:  Soft Computing and Applications L-T-P: 3-0-0 Credit: 3
Pre-requisite(s):  Nil
Artificial Neural Networks (Theory and Applications): Single and multi-layer artificial neural networks, radial basis function networks, recurrent neural network, functional link artificial neural networks. Fuzzy logic (Theory and applications): Mamdani fuzzy models, T-S fuzzy model, neuro-fuzzy systems, ANFIS. Evolutionary computing (Algorithms and Applications): Genetic algorithms and variants, Differential evolution, Particle swarm optimization (PSO) and variants, Bacterial foraging optimization (BFO), Ant colony optimization - travelling salesman problem, Artificial immune systems, cat swarm optimization. Multi-objective evolutionary algorithms: NSGA –II, multi-objective PSO and variants. Texts/Reference Books:
  1. S. Haykin, ‘Neural Networks and Learning Machines’, Prentice Hall, 2009.
  2. Y.H. Pao, ‘Adaptive pattern recognition and neural networks’, Addison-Wesley, 1989.
  3. Rich, E., Knight, K. and Nair, S.B., ‘Artificial Intelligence’, 3rd Ed., Tata McGraw Hill. 2009
  4. Deb, K., ‘Optimization for Engineering Design Algorithms and Examples’, Prentice Hall of India. 2009.
  5. Jang, J.S.R., Sun, C.T. and Mizutani, E., ‘Neuro-fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence’, Prentice Hall, 2009.
  6. Hagan, M., ‘Neural Network Design’, Nelson Candad, 2008.
  7. K.A.D. Jong, ‘Evolutionary Computation – A Unified Approach’, PHI Learning, 2009.
  8. Research publications that will be suggested during the course.