Logo ČVUT
CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2018/2019

Algorithms and Structures of Neurocomputers

Login to KOS for course enrollment Display time-table
Code Completion Credits Range Language
A0M31ASN Z,ZK 5 2+2c Czech
Lecturer:
Jana Tučková (guarantor)
Tutor:
Jana Tučková (guarantor)
Supervisor:
Department of Circuit Theory
Synopsis:

Information about the basic principles and possibility of the application of the neural informative technology for the signal processing are the main topic. The lectures are devoted to the introduction into the artificial neural networks (NN) theory and applications, to the choice and the optimisation of the structures, the choice of the data, and to the solutions of the classification. The neural network applications at the speech and image processing are investigated in detail. Some neural network applications in the biomedical engineering and hardware realization of the SOM are described. The applications are o focused to EEG and ECG processing, also to possibilities of applications ANN at physiotherapy,

Requirements:

Basic knowledge of the speech and image processing, MATLAB, probability calculus and statistics applications. Active participation on the seminars, develop semester project. More on http://amber.feld.cvut.cz/SSC

Syllabus of lectures:

1. Neural networks - research history, biological and artificial NN, applications

for signal processing, neural models, activation functions.

2. Learning principles, Self-Organizing Maps (SOM), Kohonen's maps.

3. Supervised SOM, U-matrix, LVQ classifier.

4. Multilayer networks (feedforward and Elman networks) with Back-Propagation learning algorithm (BPG).

5. Basic BPG, modifications.

6. Deep neural networks.

7. Optimisation of the structure, Data Mining. neural network pruning, Input data choice.

8. Support vector machine learning.

9. ANN and prediction and classification.

10. ANN applications in speech processing and emotion analysis. Basic terms of phonetics, characteristics

of the spoken speech (mormal and pathological).

11. Speech synthesizer. Image recognition.

12. ANN applications in neurology and rehabilitation medicine and in selected medical branches.

13. Special structures (CNN, TDNN, Wavelet NN, fuzzy-neuron networks). Genetic algorithm.

14. ANN realizations. neurocomputers. The others ANN applications.

Syllabus of tutorials:

1. Introduction, MATLAB, NN-Toolbox fundamentals, information of the semester

projects.

2. ANN basic function, Perceptron, ADALINE, MADALINE, LMS algorithm.

3. Self-Organizing Maps, supervised SOM, U-matrix. SOM Toolbox.

4. Kohonen's maps, LVQ algorithms - NN Toolbox, MATLAB.

5. Multilayer neural networks. Assignment of the semester projects.

6. Modifications of the BPG algorithm.

7. Deep neural networks.

8. Speech Laboratory - experiments. Assignment of the semester projects.

9. Presentation of the semester project thesis - control.

10. Pruning - ANN optimisation. Semester projects - consultations.

11. Experiments with neural network parameters. Semester projects - consultations.

12. Experiments with SOM Toolbox . Semester projects - consultations.

13. Semester projects - consultations.

14. Semester projects - evaluation, credits.

Study Objective:

Students acquire real experiences with the Neural Network Toolboxu from MATLAB for their project applications with MLNN and SOM. Our goal is the possibility to the familiarize with the perspective topics currented in the foreign countries. Our aim is also help to students with diploma work topic choice. The signal processing by artificial neural networks (normal or pathological speech and emotion analyses, recognition and synthesis) will be study.

Study materials:

1. Kohonen,T.: Self-Organizing Maps. Berlin Heidelberg, 3rd Edition, Springer Series in Information Sciences, Springer-Verlag, 2001, ISBN 3-540-67921-9.

2. Handbook of Neural Network Signal Processing.The Electrical Engineering and Applied Signal Processing Series. Ed.: Yu Hen Hu, Jenq-Neng Hwang. CRC Press, USA,2002, ISBN 0-8493-2359-2.

3. Haykin, S.: Neural Networks. A Comprehensive Foundation. Macmillan College Publishing Company, Inc. USA, 1994. 2nd.ed. 1998, Prentice/Hall, Upper Saddle River, NJ.

4. Program library SOM Toolbox 2.0. www.cis.hut.fi/projects/somtoolbox/download

Note:
Further information:
https://moodle.fel.cvut.cz/courses/A0M31ASN http://amber.feld.cvut.cz/ssc
Time-table for winter semester 2018/2019:
06:00–08:0008:00–10:0010:00–12:0012:00–14:0014:00–16:0016:00–18:0018:00–20:0020:00–22:0022:00–24:00
Mon
Tue
roomT2:A4-405
Tučková J.
09:15–10:45
(lecture parallel1)
Dejvice
Laborator
Fri
Thu
roomT2:A4-405
Tučková J.
09:15–10:45
(lecture parallel1
parallel nr.101)

Dejvice
Laborator
Fri
Time-table for summer semester 2018/2019:
Time-table is not available yet
The course is a part of the following study plans:
Data valid to 2019-07-23
For updated information see http://bilakniha.cvut.cz/en/predmet12526304.html