FI:P069 Hybrid Systems of Machine Lear

P069 Hybrid Systems of Machine Learning

Faculty of Informatics
Spring 1998

Extent and Intensity

2/1. 3 credit(s). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).

Teacher(s)

doc. Ing. Jan Žižka, CSc. (lecturer)

Guaranteed by

Contact Person: doc. Ing. Jan Žižka, CSc.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

Syllabus

Hybrid neural networks, a fuzzy perceptron, an AND/OR fuzzy neuron with t-norms and s-norms. Fuzzy neural networks.
NEFCON and ANFIS architectures of hybrid neural networks. Neuro-fuzzy classifiers. Optimization of fuzzy rules by neural networks and genetic algorithms.
Genetic algorithms and simulated annealing combined with neural networks, optimization of neural networks. Nonlinear dynamical systems, chaos, strange attractors. Recurrent networks, Hopfield networks.
Applications of decision trees to the setup of neural network weights.
Fuzzy-genetic modelling. Genetic programming.
Application examples.

Language of instruction

Czech

The course is also listed under the following terms Spring 1997, Spring 2001, Spring 2002.