Neural Networks and Neurocomputers
Code | Completion | Credits | Range |
---|---|---|---|
E36NAN | Z,ZK | 4 | 2+2s |
- The course is a substitute for:
- Neural Nets and Neurocomputers (XE36NAN)
- Lecturer:
- Tutor:
- Supervisor:
- Department of Computer Science and Engineering
- Synopsis:
-
Introduction, basic terminology. Hopfield's net. Perceptron, multilevel perceptron, Adaline/Madaline. Back-propagation. Kohonen's net, LVQ1, LVQ2, LVQ3. ART net, Carpenter-Grossberg classifier. Boltzmann machine. Applications: prediction by means of neural nets, expert systems, image processing, data compression. Neural hardware, neurochips, neural accelerators. Software simulators, introduction to NeuralWorks Professional II/Plus.
- Requirements:
- Syllabus of lectures:
-
1. Introduction into neural nets
2. Hopfield´s net
3. Back-propagation neural net
4. Kohonen´s net
5. Simulation, SW products
6. Paradigm classification
7. Time series prediction
8. Neural nets and image processing
9. Neural regulators
10. Selforganizing neural nets
11. Data compression
12. HW accelerators
13. Boltzman machine
14. Reserve
- Syllabus of tutorials:
-
1. - 13. Will be organized according the number of participants (Individual neural net project implemented in NEURALWORKS PROFESSIONAL II/PLUS)
14. Credit
- Study Objective:
- Study materials:
-
[1] Haykin, S.: Neural Networks. IEEE Computer Society Press 1994
- Note:
- Further information:
- No time-table has been prepared for this course
- The course is a part of the following study plans: