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State of the art speech features used during the Parkinson disease diagnosis
Bílý, Ondřej ; Smékal, Zdeněk (referee) ; Mekyska, Jiří (advisor)
This work deals with the diagnosis of Parkinson's disease by analyzing the speech signal. At the beginning of this work there is described speech signal production. The following is a description of the speech signal analysis, its preparation and subsequent feature extraction. Next there is described Parkinson's disease and change of the speech signal by this disability. The following describes the symptoms, which are used for the diagnosis of Parkinson's disease (FCR, VSA, VOT, etc.). Another part of the work deals with the selection and reduction symptoms using the learning algorithms (SVM, ANN, k-NN) and their subsequent evaluation. In the last part of the thesis is described a program to count symptoms. Further is described selection and the end evaluated all the result.
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Emotional State Recognition and Classification Based on Speech Signal Analysis
Černý, Lukáš ; Atassi, Hicham (referee) ; Smékal, Zdeněk (advisor)
The diploma thesis focuses on classification of emotions. Thesis deals about parameterization of sounds files by suprasegment and segment methods with regard for next used of these methods. Berlin database is used. This database includes many of sounds records with emotions. Parameterization creates files, which are divided to two parts. First part is used for training and second part is used for testing. Point of interest is self-organization network. Thesis includes Matlab´s program which can be used for parameterization of any database. Data are classified by self-organization network after parameterization. Results of hits rates are presented at the end of this diploma thesis.
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Network Element with Advanced Control
Zedníček, Petr ; Kacálek, Jan (referee) ; Škorpil, Vladislav (advisor)
The diploma thesis deal with finding and testing neural networks, whose characteristics and parameters suitable for the active management of network element. Solves optimization task priority switching of data units from input to output. Work is focused largely on the use of Hopfield and Kohonen networks and their optimization. Result of this work are two models. The first theory is solved in Matlab, where each comparing the theoretical results of neural networks. The second model is a realistic model of the active element designed in Simulink
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Optimization of Active Network Element Control
Přecechtěl, Roman ; Kacálek, Jan (referee) ; Škorpil, Vladislav (advisor)
The thesis deals with the use of neuronal networks for the control of telecommunication network elements. The aim of the thesis is to create a simulation model of network element with switching array with memory, in which the optimization kontrol switching array is solved by means of the neural network. All source code is created in integrated environment MATLAB. To training are used feed-forward backpropagation network. Miss achieve satisfactory result mistakes. Work apposite decision procedure given to problem and it is possible on ni tie up in an effort to find optimum solving.
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Design of algorithms for neural networks controlling a network element
Stískal, Břetislav ; Kacálek, Jan (referee) ; Škorpil, Vladislav (advisor)
This diploma thesis is devided into theoretic and practice parts. Theoretic part contains basic information about history and development of Artificial Neural Networks (ANN) from last century till present. Prove of the theoretic section is discussed in the practice part, for example learning, training each types of topology of artificial neural networks on some specifics works. Simulation of this networks and then describing results. Aim of thesis is simulation of the active networks element controlling by artificial neural networks. It means learning, training and simulation of designed neural network. This section contains algorithm of ports switching by address with Hopfield's networks, which used solution of typical Trade Salesman Problem (TSP). Next point is to sketch problems with optimalization and their solutions. Hopfield's topology is compared with Recurrent topology of neural networks (Elman's and Layer Recurrent's topology) their main differents, their advantages and disadvantages and supposed their solution of optimalization in controlling of network's switch. From thesis experience is introduced solution with controll function of ANN in active networks elements in the future.
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Modelling and simulation in the field of waste management
Pařízková, Iva ; Popela, Pavel (referee) ; Touš, Michal (advisor)
This bachelor thesis is focused on the application of multilayer perceptron net for modelling the technolgical units of waste-to-energy facility ZEVO Malesice. It was specifically created to model the amount of steam generated in steam-boilers and to quantify the consumpion of steam by an external subject. Firstly, the basics of neuron theory are presented. In the following, a Statistica artificial neural network module is described. This module was used to develop the neural network models. The models appearing in practical section were created with the use of STATISTICA software. Last chapter deals with detailed description of the developed models and their comparison with simple linear and nonlinear regression models. Last but not least, the description of a software providing easy implementation of neural network models into Visual Basic for Application programming language is presented.
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Suitability Assessment of Learning for Heuristic Adaptive Control of Drives
Kerek, Milan ; Krejsa, Jiří (referee) ; Březina, Tomáš (advisor)
The bachelor thesis is aimed to explore the possibilities of using artificial neural network in order to controll nonlinear dynamic systems. In addition the document shows the options to combine neural controllers with linear controllers, such as PID regulator, state space regulator with compensation error. Simulation models were designed in environment of Matlab/Simulink. Neural networks were exploit with the help of Neural Network Toolbox-u. Designed regulators were tested on regulating angular velocity of nonlinear system of 2nd order – wound DC motor.
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An efficiency comparison of simulation methods for artificial neural network training and inverse analysis
Nezval, Michal ; Novák, Drahomír (referee) ; Lehký, David (advisor)
The thesis deals with inverse analysis which is based on combination of artificial neural network and stochastic methods. The goal is to compare an efficiency of new simulation method Hierarchical Subset Latin Hypercube Sampling to classical Monte Carlo method and standard Latin Hypercube Sampling method used for neural network training. The efficiency is compared for a different neural network structures. The inverse analysis is then applied for engineering tasks – identification of limit state fiction parameters related to pitched-roof frame and material parameters of concrete specimen subjected to three-point bending. Finally an efficiency of Hierarchical Subset Latin Hypercube method comparing to Monte Carlo and Latin Hypercube Sampling methods is discussed.
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Artificial neural networks for learning robots
Sovka, Michal ; Jirků, Petr (advisor) ; Berka, Petr (referee)
The main aim of this bachelor thesis is to understand, describe and explain the basic principles and elements used in learning robots with using artificial neural networks. Firstly, I focus on robots in general and their basic functional units. Then it is to introduce the theory of learning applied to real environments. Nervous system in this work becomes a central learning muse about theory with using artificial neural networks. It's very important in understanding of artificial neurons and artificial neural networks as a complex I consider the biological neuron and its synapses. That's why I take them very seriously. After introduction to artificial neural networks I attend to only the one of their group fully used in robotics. Finally, I demonstrate the function of Kohonen type of artificial neural network used in robotics and scilicet in the application developed in one of a foreign university workplace.I hope the benefits of my work is in a comprehensive text focused on the basic elements of robotics, as well as artificial neural networks and their neurobiological assumption. The work can then be used in studying for people interested in a broader approach to intelligent robotics.
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