Aims & Learning Objectives:
Aims: To provide the fundamental principles of various artificial intelligent techniques and insight of how to apply those techniques to solve practical problems.
Objectives:
After completing this module, students should be able to: distinguish the differences between intelligent techniques and conventional techniques; be aware of the opportunities where intelligent techniques might be most beneficial; be able to construct simple intelligent systems to solve practical problems; be able to further enhance the performances of intelligent techniques.
Content:
Expert Systems (ES): major characteristics of expert systems; knowledge representation techniques; inference techniques; rule-based expert systems; applications in power systems.
Fuzzy Logic (FL): fuzzy set theory; fuzzy inference; fuzzy logic system; fuzzy control; applications in power systems.
Neural Networks (NS): artificial neurons and neural networks; learning process: Error-correction learning, Hebbian learning, Boltzmann learning, competitive learning, supervised/unsupervised learning; Perception and multilayer perception; self-organising Kohonen networks; Hopfield neural networks; practical implementation and applications.
Hybrid systems: typical hybrid intelligent techniques, applications in power systems.
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