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Neural Networks for Protein Structure Predictions
Šamšula, Radek ; Bartík, Vladimír (referee) ; Burget, Radek (advisor)
Bachelor's thesis studies the problems of neural networks and prediction of protein secondary structure. This thesis is focused on multilayer neural network with back-propagation learning algorithm and their use for prediction. It describes the infuence of network architecture and their parameters settings on the prediction results. Furthermore suitability of the network for this kind of prediction is discused.
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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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Playing Gomoku with Neural Networks
Slávka, Michal ; Kolář, Martin (referee) ; Hradiš, Michal (advisor)
Táto práca sa zaoberá použitím algoritmu AlphaZero pre hru Gomoku. AlphaZero je založený na spätnoväzbnom učení a k trénovaniu nemusia byť využité žiadne existujúce datasety. Trénovanie prebieha iba na hrách algoritmu samého so sebou. AlphaZero používa algoritmus na prehľadávanie stromu, pre zlepšenie stratégie. Na vylepšnej stratégii sa následne trénuje neurónová sieť. Tento prístup bol úspešný v hrách proti existujúcim algoritmom. Generovanie trénovacích dát vysokej kvality si vyžaduje veľa výpočetne náročných iterácií trénovania a generovania dát. Experimenty ukázali, že každou iteráciou sa algoritmus zlepšuje, čo naznačuje, že je ešte miesto na zlepšenie, ale množstvo iterácií nedostačovalo na to, aby bol poriadne natrénovaný.
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Neural-Fuzzy Systems
Dalecký, Štěpán ; Samek, Jan (referee) ; Zbořil, František (advisor)
The thesis deals with artificial neural networks theory. Subsequently, fuzzy sets are being described and fuzzy logic is explained. The hybrid neuro-fuzzy system stemming from ANFIS system is designed on the basis of artificial neural networks, fuzzy sets and fuzzy logic. The upper-mentioned systems' functionality has been demonstrated on an inverted pendulum controlling problem. The three controllers have been designed for the controlling needs - the first one is on the basis of artificial neural networks, the second is a fuzzy one, and the third is based on ANFIS system. The thesis is aimed at comparing the described systems, which the controllers have been designed on the basis of, and evaluating the hybrid neuro-fuzzy system ANFIS contribution in comparison with particular theory solutions. Finally, some experiments with the systems are demonstrated and findings are assessed.
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Using artificial intelligence to monitor the state of the machine
Kubisz, Jan ; Kroupa, Jiří (referee) ; Kovář, Jiří (advisor)
Diploma thesis focus on creation of neural network’s internal structure with goal of creation Artificial Neural Network capable of machine state monitoring and predicting its remaining usefull life. Main goal is creation of algorithm’s and library for design and learning of Artificial Neural Network, and deeper understanding of the problematics in the process, then by utilising existing libraries. Selected method was forward-propagation network with multi-layered perceptron architecture, and backpropagation learning. Achieved results was, that the network was able to determine parts state from vibration measurement and on its basis predict remaining usefull life.
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Comparison of Libraries of Artificial Neural Networks
Dohnal, Zdeněk ; Zbořil, František (referee) ; Dalecký, Štěpán (advisor)
This thesis is about comparison of libraries of artificial neural networks. Basic theory of neuron, neural networks and their learning algorithms are explained here. Multilayer perceptron, Self organizing map and Hopfield net are chosen for experiments. Criteria of comparison such as licence, community or last actualization are designed. Approximation of function, association and clustering are chosen as task for experiments. After that, there is implementation of applications using chosen libraries. At the end, result of comparison and experiment are evaluated.
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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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Recurrent Neural Networks in Computer Vision
Křepský, Jan ; Řezníček, Ivo (referee) ; Španěl, Michal (advisor)
The thesis concentrates on using recurrent neural networks in computer vision. The theoretical part describes the basic knowledge about artificial neural networks with focus on a recurrent architecture. There are presented some of possible applications of the recurrent neural networks which could be used for a solution of real problems. The practical part concentrates on face recognition from an image sequence using the Elman simple recurrent network. For training there are used the backpropagation and backpropagation through time algorithms.
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