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Competitions in Artificial Intelligence
Šafář, Pavel ; Hynčica, Tomáš (referee) ; Honzík, Petr (advisor)
My thesis is focused on the field of artificial intelligence and especially on the competitions in the areas of robotics, computer vision, communication, time series forecasting and game playing programmes. Furthermore I devoted myself to the research of the use of neural network as a tool to solve the Gomoku game problems. The neural network processes the game situations and sets up the output values based on the pre-set models.
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Time series annalyze by neural networks models
Jiráň, Robin ; Arltová, Markéta (advisor) ; Žižka, David (referee)
This thesis deals about using models of neural networks like alternative of time series model based on Box-Jenkins methodology. The work is divided into two parts according to the model construction method. Each of the parts contains a theory that explains the individual processes and the progress of the model construction. This is followed by two experiments demonstrating the difference in approach to the design of a given model and creating a forecast by estimated values. for the following year. The last part expertly evaluates the quality of the predictions and considers the use of neural networks against prediction models as an alternative to Box-Jenkins methodology based models
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Neural Network Library and Editor
Rouček, Martin ; Ježek, Pavel (advisor) ; Pešková, Klára (referee)
Neural network models are more often used in desktop applications given the increasing speed of computers. A very widespread platform for writing desktop applicatons is .NET Framework. Nevertheless, there is no neural networks library for the .NET Framework platform with a simple API and the possibility to work with library objects in a graphical interface. The author decided to create such a library. The main part of the thesis is a neural networks library GNNL that is initially limited to implementing two frequently used neural networks models which are a multilayer perceptron and self- organizing map together with learning algorithms of backpropagation and competitive learning. Graphical support of the library GNNL consists of a library GNNLV and neural network editor. The Library GNNLV contains the controls that allow working with GNNL library objects and a programmer can use them in his or hers application. The Neural network editor enables the programmer to create a neural network in a graphical interface, train it, analyze it, save it, and later use it in different applications. Text of the thesis focuses on analyzing and describing the implementation of the library with its graphical support. A major component of the text is a summary of neural networks theory for laics or programmers using library...
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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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Image classification using artificial intelligence
Labuda, Adam ; Přinosil, Jiří (referee) ; Burget, Radim (advisor)
This bachelor's thesis address the issue of classification and feature extraction of imagesfrom image. In JAVA platform will create an example that loads a set of images, extracted from symptoms with the help of artificial intelligence provided by the thesis supervisor. Artificial intellihence assumed kind of image. Finally the results are compared. }
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Data analysis from the manufacturing process
Krčmář, Martin ; Honzík, Petr (referee) ; Zezulka, František (advisor)
This thesis deals with the classification of production data using algorithms: neural networks, decision trees and naive bayesian classifier. The neural network is dedicated forward multilayer networks with a learning algorithm of backpropagation. In thesis, these algorithms are described and evaluated their pros and cons. Another part deals with the development of the program in C# for creating these algorithms. The last part is devoted to the evaluation of the results. Bachelor thesis contains a sample of generated clasification models decision tree and bayesian classifier.
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License plate recognition
Trkal, Ondřej ; Hynčica, Tomáš (referee) ; Jirsík, Václav (advisor)
This thesis deals with the recognition of license plates using neural networks with backpropagation learning. The theoretical section is a brief summary of the principle of creating a new license plate, computer vision and neural networks with backpropagation learning. The practical part describes the design of methods used to detect single-line license plates of cars in the Czech Republic. In this work has been tested several ways to describe the signs and examined the effect of these descriptions and topology of neural networks for quality license plate recognition.
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Multilayer neural network
Kačer, Petr ; Klusáček, Jan (referee) ; Jirsík, Václav (advisor)
Bachelor's thesis describes the basics of issue of multilayer neural networks and explains principle of backpropagation algorithm. Next part of thesis is about development of a software for learning and testing multilayer neural networks and describes its graphical user interface. Last part of thesis is dedicated to tutorial examples and practical demonstrations of multilayer neural network usage.
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Speed of learning multilayer network
Maceček, Aleš ; Zámečník, Dušan (referee) ; Jirsík, Václav (advisor)
Theoretical study about neural networks, especially their types of topologies and networks learning. Special attention is attended to multilayer neural network with learning backpropagation. Introduced learning algorithm backpropagation of simple networks in conjunction with descriptions of parameters affecting network learning also methods to exaluation quality of network learning. Definition moment invariants to rotation, translation and scaling. Optimalization parameters of neural networks to find the network which has the fastest learning and also the networks with the best value of recognition patterns of letters from testing set.
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Classification Mining Modules of Data Mining System on NetBeans Platform
Kuzma, Norbert ; Šuška, Boris (referee) ; Šebek, Michal (advisor)
This bachelor thesis deals with the data mining and the creation of data mining unit for data mining system, which is beeing developed at FIT. This is a client application consisting of a kernel and its graphical user interface and independent mining modules. The data mining system is implemented in Java language and its graphical user interface is built on NetBeans platform. The content of this work will be the introduction into the issue of knowledge discovery and then the presentation of neural networks, for which there will subsequently be implemented the stand-alone data mining module. Furthermore, the implementation of this modulewill be described.
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