Typically, Computational Intelligence is used as an umbrella term for the fields of
artificial neural nets (ANN), fuzzy systems (FS) and evolutionary computation (EC).
This course offers a thorough introduction into all three fields.
Foundations of ANN: McCulloch-Pitts nets, perceptrons, Hopfield nets,
supervised and unsupervised learning, backpropagation.
Foundations of FS: Fuzzy sets, fuzzy numbers, fuzzy logic, fuzzy reasoning.
Foundations of EC: Algorithmic basics, parameterization, analysis methods, limits of deployment.
Students should get an overview about the different aspects of Computational Intelligence.
They should be familiar with the essential elements in all three fields (ANN, FS, EC) and
able to deploy and adapt these methods for real applications. Moreover, they should be
able to assess when to deploy these methods and when not.
The slides about organizational issues are in the first set of slides (10.10.18) below.
Artificial Neural Networks I
Artificial Neural Networks II
Artificial Neural Networks III
Fuzzy Systems I
Fuzzy Systems II
Fuzzy Systems III
Fuzzy Systems IV
Evolutionary Algorithms I
Evolutionary Algorithms II
Evolutionary Algorithms III
Evolutionary Algorithms IV
A.E. Eiben and J.E. Smith: Introduction to Evolutionary Algorithms. Corrected 2nd printing. Springer 2007.
Raul Rojas: Neural Networks - A Systematic Introduction. Springer 1996. Available online.
G.J. Klir und B. Yuan: Fuzzy Sets and Fuzzy Logic. Prentice Hall 1995.
F. Höppner, F. Klawonn, R. Kruse und T. Runkler: Fuzzy Cluster Analysis. Wiley 1999.
Amit Konar: Computational Intelligence: Principles, Techniques and Applications. Springer 2005.