People Innovation Excellence
 

Selected Topics in Computational Intelligence I

Learning Outcomes

On successful completion of this Course, students will be able to: Analyse and differentiate the Computational Intelligence domain; Appraise the application of Artificial Neuron; Evaluate the Neural Network; Analyse and differentiate the model of Self-Organizing Feature Maps; Formulate the Reinforcement Learning; Appraise the Generic Evolutionary Algorithm; Value the Application of Canonical Genetic Algorithm; Integrate the Genetic Programming with other algorithm in Computational Intelligence; Formulate the Evolutionary Programming; Compare some Generic Evolution Strategy Algorithms; Compose the Differential Evolution; Evaluate the Fuzzy Cultural Algorithm; formulate the Cooperative Coevolution Modeling; Assemble the Particle Swarm Optimization algorithm for certain case; Value the Ant Colony Optimization Meta-Heuristic in Computational Intelligence domain; Compare some of the Natural Immune System Algorithms; Asses the Artificial Immune Models for some cases; Construct Fuzzy Sets modeling; Formulate the Fuzzy Inferencing with certain cases; Select Fuzzy Controller Types for certain problems; Evaluate the Uncertainty in Rough Sets in the Computational Case; Asses and compare the application of certain algorithm in Business; Asses and compare the application of certain algorithm in science and engineering; Asses and compare the application of certain algorithm in economics Modelling.

Topics

  1. Artificial neural networks
  2. Evolutionary computation
  3. Swarm intelligence
  4. Artificial immune systems
  5. Fuzzy systems
  6. Biological neural networks
  7. Evolution
  8. Swarm behavior of social organisms
  9. Natural immune systems
  10. Human thinking processes
  11. Value creation potential of computational intelligence
  12. Defining an implementation strategy for successful real-world applications of computational intelligence
  13. Future directions of computational intelligence

 


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