Artificial Neural Networks, Realization and Applications

Course guarantor:

Lecturer:

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Supervisor:

Department of Transport Telematics

Synopsis:

History of neural networks. Basic principles. Comparing the structure of a natural and an artificial neuron. Neural classificators, predictors, compresors, expanders and other specialised functional blocs and systems. Modelling of neurons. Grossberg's equations. Learning principles. Leyered and Hopfield's nets.

Requirements:

Basic knowledge of mathematics.

Syllabus of lectures:

Syllabus of tutorials:

Study Objective:

Basic knowledge in the field of neural networks, their structures, trainning and application. Information and control tasks in transportation and other areas based on neural networks.

Study materials:

Novák M., Kufufaki O., Moos P., Musílek P., Pelikán E., Šebesta V.: Umělé neuronové sítě, teorie a aplikace, Praha, C. H. Beck, 1998

Gupta, M. M.: Static and Dynamic Neural Network, Wiley & Sons, New Jersey, Canada, ISBN 0-471-21948-7, 2003

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Further information:

No time-table has been prepared for this course

The course is a part of the following study plans: