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Network switch optimization by means of neural network
Lýsek, Jiří ; Krček, Petr (referee) ; Šťastný, Jiří (advisor)
This thesis deals with the problem of priority network switch, the model of which was developed in the C++ language. The traffic optimization task is solved by the use of several artificial neural networks, which are described, compared to each other and then evaluated which of them is more suitable for this task. The result of this work is a model of network switch and a comparison of computational time complexity of solving the optimization problem using the artificial neural network. The thesis was developed in research project MSM 0021630529 Intelligent Systems in Automation.
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Fuzzy Neural Networks
González, Marek ; Rozman, Jaroslav (referee) ; Zbořil, František (advisor)
This thesis focuses on fuzzy neural networks. The combination of the fuzzy logic and artificial neural networks leads to the development of more robust systems. These systems are used in various field of the research, such as artificial intelligence, machine learning and control theory. First, we provide a quick overview of underlying neural networks and fuzzy systems to explain fundamental ideas that form the basis of the fields, and follow with the introduction of the fuzzy neural network theory, classification and application. Then we describe a design and a realization of the fuzzy associative memory, as an example of these systems. Finally, we benchmark the realization using the pattern recognition and control tasks. The results are evaluated and compared against existing systems.
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Neural Networks and Their Applications
Chaloupka, David ; Rozman, Jaroslav (referee) ; Zbořil, František (advisor)
The aim of this thesis is to present a consistent insight into the most frequently used types of artificial neural networks and their applications. It depicts feedforward neural networks with backpropagation training algorithm, Hopfield networks and self-organizing maps (Kohonen maps). Second part of this thesis demonstrates typical applications of described networks and discusses various factors, which influence performance of these networks on chosen tasks.
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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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Neural Networks Application
Macůrek, Miloslav ; Jindra, Petr (referee) ; Šťastný, Jiří (advisor)
This Bechelor’s thesis will cover the theme of neural networks, their history, characteristics, basic typology of neural networks and learning algorithms and analysis of their applications demonstrated in simple examples created in MATLAB software.
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Neural Networks Application
Macůrek, Miloslav ; Jindra, Petr (referee) ; Šťastný, Jiří (advisor)
This Bechelor’s thesis will cover the theme of neural networks, their history, characteristics, basic typology of neural networks and learning algorithms and analysis of their applications demonstrated in simple examples created in MATLAB software.
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|
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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.
|
|
|
Neural Networks and Their Applications
Chaloupka, David ; Rozman, Jaroslav (referee) ; Zbořil, František (advisor)
The aim of this thesis is to present a consistent insight into the most frequently used types of artificial neural networks and their applications. It depicts feedforward neural networks with backpropagation training algorithm, Hopfield networks and self-organizing maps (Kohonen maps). Second part of this thesis demonstrates typical applications of described networks and discusses various factors, which influence performance of these networks on chosen tasks.
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|
|
Fuzzy Neural Networks
González, Marek ; Rozman, Jaroslav (referee) ; Zbořil, František (advisor)
This thesis focuses on fuzzy neural networks. The combination of the fuzzy logic and artificial neural networks leads to the development of more robust systems. These systems are used in various field of the research, such as artificial intelligence, machine learning and control theory. First, we provide a quick overview of underlying neural networks and fuzzy systems to explain fundamental ideas that form the basis of the fields, and follow with the introduction of the fuzzy neural network theory, classification and application. Then we describe a design and a realization of the fuzzy associative memory, as an example of these systems. Finally, we benchmark the realization using the pattern recognition and control tasks. The results are evaluated and compared against existing systems.
|
|
|
Network switch optimization by means of neural network
Lýsek, Jiří ; Krček, Petr (referee) ; Šťastný, Jiří (advisor)
This thesis deals with the problem of priority network switch, the model of which was developed in the C++ language. The traffic optimization task is solved by the use of several artificial neural networks, which are described, compared to each other and then evaluated which of them is more suitable for this task. The result of this work is a model of network switch and a comparison of computational time complexity of solving the optimization problem using the artificial neural network. The thesis was developed in research project MSM 0021630529 Intelligent Systems in Automation.
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