Course content

The course presents a systematic introduction to the fundamentals and practices of Computational Intelligence Introduction, which encompasses Artificial neural networks, Fuzzy logic systems, Evolutionary computing, Swarm intelligence, Neuro-fuzzy and Fuzzy neural systems, hybrid intelligent systems and applications to design, manufacturing and business.

Learning outcome

Knowledge, the candidate has knowledge about: - Computational intelligence is the study of adaptive mechanisms to enable or facilitate intelligent behaviour in complex and changing environments. Skills, the student will have skills related to: - The ability to use Computational Intelligence to solve engineering problems, which cannot be solved by traditional mathematical approaches. General competence, the candidate can: - Understand meta-heuristic approaches as an important method of industrial and commercial systems and in the public administration.

Learning methods and activities

Lectures and seminar. To pass the course a score of at least 70 percent is required.

Compulsory assignments

• Exercises and project

Recommended previous knowledge

Knowledge about Artificial neural networks, Fuzzy logic system, Genetic algorithms and product design and process.

Required previous knowledge

Applied Coputational Intelligence in Intelligent Manufacturing.

Course materials

Kesheng Wang: Applied Computational Intelligence in Intelligent Manufacturing, Lecture Notes, 2001.
Andries P. Engelbrecht: Computational Intelligence - An introduction, Wiley, 2003.
Wang, Kesheng: Swarm intelligence in manufacturing - Principles, Applications and future trends, IWAMA2010, pp. 9-28, Tapir Academic Press, 2010.

Credit reductions