Course Syllabus
Introduction to Computational Intelligence, Intelligence machines, Computational intelligence paradigms, Rule-Based Expert Systems and Fuzzy Expert Systems, Rule-based expert systems, Uncertainty management, Fuzzy sets and operations of fuzzy sets, Fuzzy rules and fuzzy inference, Fuzzy expert systems
Case study: fuzzy logic controller for washing machines, Artificial Neural Networks
Fundamental neuro computing concepts: artificial neurons, activation functions, neural network architectures, learning rules.
Supervised learning neural networks: multi-layer feed forward neural networks, simple recurrent neural networks, time-delay neural networks, supervised learning algorithms
Unsupervised learning neural networks: self-organizing feature maps, Radial basis function networks, Deep neural networks and learning algorithms.
Case study: anomaly detection, Evolutionary computation, Chromosomes, fitness functions, and selection mechanisms.
Genetic algorithms: crossover and mutation, Genetic programming, Evolution strategies, probabilistic reasoning, Hybrid Intelligent Systems, Neural expert systems, Neuro-fuzzy systems, Evolutionary neural networks, Case study and Simulation of artificial intelligence, fuzzy evolutionary algorithms in power system applications.