AD-702 (C) · Computational Intelligence/Official Syllabus
Computational Intelligence (AD-702 (C)) - Official Syllabus
1
Unit 1
Introduction to Computational Intelligence
- Types of Computational Intelligence
- Components of Computational Intelligence
- Concept of Learning, Training model
- Parametric Models
- Nonparametric Models
- Feed Forward network
- Feedback network
2
Unit 2
Fuzzy Systems
- Fuzzy sets and operations
- Membership Functions
- Concept of Fuzzy relations and their composition
- Concept of Fuzzy Measures
- Fuzzy Rules, Inferencing
- Fuzzy Control - Selection of Membership Functions
- Fuzzyfication
- Rule Based Design & Inferencing
- Defuzzyfication
3
Unit 3
Genetic Algorithms
- Basic Genetics, Concepts
- Working Principle
- Creation of Offspring
- Encoding
- Fitness Function
- Selection Functions
- Genetic Operators-Reproduction
- Crossover
- Mutation
- Genetic Modelling, Benefits
4
Unit 4
Rough Set Theory
- Introduction, Fundamental Concepts
- Set approximation
- Rough membership
- Attributes, Optimization
- Hidden Markov Models
- Decision tree model
5
Unit 5
Introduction to Swarm Intelligence
- Swarm Intelligence Techniques
- Ant Colony Optimization
- Particle Swarm Optimization
- Bee Colony Optimization
- Applications of Computational Intelligence
Reference Books
- Computational Intelligence: Concepts to Implementations — Russell C. Eberhart, Yuhui Shi
- Computational Intelligence: An Introduction — Andries P. Engelbrecht
- Neural Networks: A Comprehensive Foundation — Simon Haykin
- Genetic Algorithm in Search Optimization and Machine Learning — David E. Goldberg
- Evolutionary and Swarm Intelligence Algorithms — Jagdish Chand Bansal, Pramod Kumar Singh, Nikhil R. Pal
- Neural Networks, Fuzzy Logic, Genetic Algorithms Synthesis and Applications — S. Rajasekaran, G.A. Vijaylakshmi Pai
- Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning & Machine Intelligence — J.S. Roger Jang, C.T. Sun, E. Mizutani
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