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Kohonen network
Fic, Miloslav ; Hynčica, Tomáš (referee) ; Jirsík, Václav (advisor)
This Bachelor’s thesis deals with self-organizing networks and its learning mechanism. The activation, adaptation and application of Kohonen network are discussed in this thesis. The program Kohonen neural network is described. The practical part of this work analyzes effect of learning parameters choice on final state of Kohonen network and how do this learning parameters affect learning process. The effect of weight vector initialization on the final best-matching neuron “position” is analyzed.
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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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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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Kohonen network
Fic, Miloslav ; Hynčica, Tomáš (referee) ; Jirsík, Václav (advisor)
This Bachelor’s thesis deals with self-organizing networks and its learning mechanism. The activation, adaptation and application of Kohonen network are discussed in this thesis. The program Kohonen neural network is described. The practical part of this work analyzes effect of learning parameters choice on final state of Kohonen network and how do this learning parameters affect learning process. The effect of weight vector initialization on the final best-matching neuron “position” is analyzed.
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