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Data Classification using Artificial Neural Networks
Gurecká, Hana ; Dvořák, Jiří (referee) ; Matoušek, Radomil (advisor)
The thesis deals with neural networks used in data classification. The theoretical part presents the three basic types of neural networks used in data classification. These networks are feedforward neural network with backpropagation algorithm, the Hopfield network with minimization of energy function and the Kohonen’s method of self-organizing maps. In the second part of the thesis these algorithms are programmed and tested in Matlab environment. At the end of each network testing results are discussed.
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Intelligent Client for Music Player Daemon
Wagner, Tomáš ; Kočí, Radek (referee) ; Janoušek, Vladimír (advisor)
The content of this master thesis project is about design and implementation of intelligent client application for Music Player Daemon (MPD), which searches and presents the metadata related to played content. The actual design precedes the theoretical analysis, which includes analysis of agent systems, methods of data classification, web communication protocols and languages for describing HTML document. At the same time is analyzed the MPD server and communication protocol used by clients application. Furthermore, this work describes the current client applications that presents metadata. In the last chapters of the thesis describes the design and implementation of intelligent client. It describes the methods of solution the implementation and solution of problems. Lastest chapters describes the testing result.
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Recognition of electrochemical signals using artificial neuronal network
Šílený, Jan ; Kuchta, Radek (referee) ; Hubálek, Jaromír (advisor)
Automatical electrochemical measurements are sources of large data sets intended for further analysis. This work deals with classification, evaluation and processing of electrochemical signals using artificial neural networks. Due to high dimensionality of input data, an autoassociative neural network (AANN) is used in this work. This type of network performs dimensionality reduction via filtering the input data into relatively small number of principal parameters at the bottleneck output. These extracted parameters can be used for classification, evaluation and additional modelling of analyzed data trough the reconstructive part of this network. Furthermore, this work deals with implementation of a feedforward neural network in OpenCL language.
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Visualization of Data
Nečesal, Petr ; Komárková, Lenka (advisor) ; Bína, Vladislav (referee)
The bachelor thesis is focused on the most common and basic visualization methods. The document puts the emphasis on statistical data visualization. Data are classified according to their types. There are main reasons for visualization, there are also factors which may influence the subjective impression among other things. The thesis is divided into several chapters that correspond to types and dimensions of data to be visualized. The general principles of graphical methods are listed in the text. Graphs, charts and other visualization tools are described and created for each type of data by means of several software tools. The last chapter describes choosen examples of charts and graphs used in management.
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