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Adaptation of parameters in fuzzy systems
Fic, Miloslav ; Jura, Pavel (referee) ; Jirsík, Václav (advisor)
This Master’s thesis deals with adaptation of fuzzy system parameters with main aim on artificial neural network. Current knowledge of methods connecting fuzzy systems and artificial neural networks is discussed in the search part of this work. The search in Student’s works is discussed either. Chapter focused on methods application deals with classifying ability verification of the chosen fuzzy-neural network with Kohonen learning algorithm. Later the model of fuzzy system with parameters adaptation based on fuzzyneural network with Kohonen learning algorithm is shown.
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Visual Simulator of General Neural Networks
Herman, David ; Zbořil, František (referee) ; Martinek, David (advisor)
The subject of this bachelor thesis is the design of a general library of neural networks. Another subject is the implementation of a visual simulator, which would represent graphically, in a suitable manner, the algorithm of learning and the active dynamics of the network, in separate steps. This application also has to be platform independent.
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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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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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Indoor Robot - Control Neural Network
Křepelka, Pavel ; Kopečný, Lukáš (referee) ; Žalud, Luděk (advisor)
In this document, I describe possibilities of mobile robot navigation. This problems are solving many different ways, but there isn’t satisfactorily result to this day. You find there describe of deterministic algorithms, this algorithms can be used for simply actions like obstacle avoiding or travel in corridor. For global navigation this algorithms fails. In next part of document is theory of artificial neural nets (perceptron, multi layer neural nets, self organization map) and using them in mobile robots. Own navigation algorithms was tested on constructed mobile robot or simulated in SW described in chapter 6. Design own control algorithms is based on neural net (Kohonen net). Designed algorithms can be used for one-point navigation or complex global navigation. In document, there is comparing of various ways to navigation, their advantages and disadvantages. Goal of this document is find effective algorithm for navigation and artificial intelligence appears to be the right solution.
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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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Neural Network Based Image Segmentation
Jamborová, Soňa ; Řezníček, Ivo (referee) ; Žák, Pavel (advisor)
This work is about suggestion of the software for neural network based image segmentation. It defines basic terms for this topics. It is focusing mainly at preperation imaging information for image segmentation using neural network. It describes and compares different aproaches for image segmentation.
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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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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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Neural Network Based Image Segmentation
Jamborová, Soňa ; Řezníček, Ivo (referee) ; Žák, Pavel (advisor)
This work is about suggestion of the software for neural network based image segmentation. It defines basic terms for this topics. It is focusing mainly at preperation imaging information for image segmentation using neural network. It describes and compares different aproaches for image segmentation.
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