SubjectsSubjects(version: 894)
Course, academic year 2021/2022
  
Neural Networks - M445004
Title: Neuronové sítě
Guaranteed by: Department of Computing and Control Engineering (445)
Actual: from 2020
Semester: summer
Points: summer s.:5
E-Credits: summer s.:5
Examination process: summer s.:
Hours per week, examination: summer s.:2/2 C+Ex [hours/week]
Capacity: unknown / unknown (unknown)
Min. number of students: unlimited
Language: Czech
Teaching methods: full-time
Level:  
For type: Master's (post-Bachelor)
Note: course can be enrolled in outside the study plan
enabled for web enrollment
Guarantor: Procházka Aleš prof. Ing. CSc.
Mudrová Martina Ing. Ph.D.
Incompatibility : AM445004
Interchangeability : AM445004, N445024
Is incompatible with: AM445004
Is interchangeable with: AM445004
Annotation -
Last update: Kubová Petra Ing. (12.04.2018)
The subject presents mathematical models of physiological neural networks and their optimization using evaluated and target values. Problems of global and local optima are presented on the error surface together with the least square method and gradient method for evaluation of optimal structure and network coefficients. Application include signal denoising, prediction and classification with the use in engineering and biomedicine.
Aim of the course - Czech
Last update: Kubová Petra Ing. (29.06.2020)

Studenti budou umět (i) matematicky modelovat vícevrstvé a rekurentní neuronové sítě, (ii) tvořit matice vzorů pro aplikace umělých neuronových sítí, (iii) optimalizovat matematické modely neuronových sítí pro potřeby klasifikace dat a (iv) využít neuronové sítě pro adaptivní potlačování rušivých složek signálů a pro jejich predikci

Literature -
Last update: Kubová Petra Ing. (12.04.2018)

[1] S. Haykin: Neural Networks, IEEE Press, 1994

Learning resources - Czech
Last update: Soušková Hana Ing. Ph.D. (25.04.2018)

http://uprt.vscht.cz/prochazka/pedag/lectures/SP0_MATLAB_2006EN.pdf

Requirements to the exam
Last update: Kubová Petra Ing. (12.04.2018)

During the term three projects are solved and they include application of artificial neural networks for biomedical signal denoising, prediction of environmental data and classification of biomedical data segments. Oral exam includes detail discussion of selected problems and their solution in the MATLAB environment.

Syllabus -
Last update: Kubová Petra Ing. (12.04.2018)

1. Fundamentals of MATLAB environment, basic operations, data files processing

2. Visualization tools in MATLAB, symbolic mathematics, principles of SIMULINK

3. Basic mathematical models of neurons, transfer functions, threshold, error surface evaluation

4. Single-layer networks, perceptron learning rule, application to classification problems

5. Adaptive linear networks, Widrow-Hoff learning rule, training and coefficients optimization

6. Neural networks in adaptive noise cancellation

7. Multi-layer networks, error function, optimization of parameters, gradient descent method

8. Basic optimization methods (backpropagation, Levenberg-Marquardt algorithm)

9. Neural networks in signal prediction and system modeling, learning and generalization

10. Radial basis networks, transfer functions definition, network structure optimization

11. Associative learning rules, self-organizing networks and maps, Hebian learning, Kohonen rule

12. Neural networks in segmentation, feature extraction and classification, competitive learning

13. Simulation of artificial neural networks in the SIMULINK environment

14. Neural networks in system identification and control, supervised and unsupervised learning

Registration requirements - Czech
Last update: Soušková Hana Ing. Ph.D. (25.04.2018)

Číslicové zpracování signálů a obrazů

Course completion requirements -
Last update: Procházka Aleš prof. Ing. CSc. (26.04.2018)

Submission of three individual projects specified during the term and oral discussion to selected research areas.

Teaching methods
Activity Credits Hours
Konzultace s vyučujícími 0,5 14
Účast na přednáškách 1 28
Příprava na přednášky, semináře, laboratoře, exkurzi nebo praxi 0,5 14
Práce na individuálním projektu 1 28
Příprava na zkoušku a její absolvování 1 28
Účast na seminářích 1 28
5 / 5 140 / 140
 
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