Neural Networks and Neurocomputers

Lecturer:

Miroslav Šnorek (gar.)

Tutor:

Miroslav Šnorek (gar.)

Supervisor:

Department of Computer Science and Engineering

Synopsis:

Theoretical background, paradigm classification and artificial neural networks learning methods. Student is supposed to propose and test the application of an artificial neural network for a partial issue concerning his dissertation theme during the semester. Procedure and results would be concluded in the preliminary publication form designed to be presentable on a scientific forum.

Requirements:

Syllabus of lectures:

Syllabus of tutorials:

Study Objective:

Study materials:

Chen, C.H.: Fuzzy Logic and Neural Network Handbook McGraw-Hill,

ISBN 0-07-011189-8, 1996

Note:

Time-table for winter semester 2011/2012:

Time-table is not available yet

Time-table for summer semester 2011/2012:

Time-table is not available yet

The course is a part of the following study plans:

• Doctoral studies, daily studies (compulsory elective course)
• Doctoral studies, combined studies (compulsory elective course)
• Doctoral studies, structured daily studies (compulsory elective course)
• Doctoral studies, structured combined studies (compulsory elective course)