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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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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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Building deep networks using autoencoders
Lohniský, Michal ; Veselý, Karel (referee) ; Hradiš, Michal (advisor)
This thesis deals with pretraining deep networks by autoencoders. Components of neural networks are described in first chapters. Rest of chapters aims to deep network trainings and to results of experiments where autoencoder pretraining and Backpropagation algorithm are compared. Results showed positive contribution of autoencoder pretraining, mainly in combination with Finetuning.
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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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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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The Betting Agent
Bělohlávek, Jiří ; Rozman, Jaroslav (referee) ; Grulich, Lukáš (advisor)
This master thesis deals with design and implementation of betting agent. It covers issues such as theoretical background of an online betting, probability and statistics. In its first part it is focused on data mining and explains the principle of knowledge mining form data warehouses and certain methods suitable for different types of tasks. Second, it is concerned with neural networks and algorithm of back-propagation. All the findings are demonstrated on and supported by graphs and histograms of data analysis, made via SAS Enterprise Miner program. In conclusion, the thesis summarizes all the results and offers specific methods of extension of the agent.
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Picture symbol identification with the aid of neural network
Pavlík, Daniel ; Burget, Radim (referee) ; Kohoutek, Michal (advisor)
This thesis is about using neural networks in recognition of letters A to Z and numbers 0 to 9. In the first part is theoretically described substance of neural networks and concretically described principle the method of learning multiple-layer network with backward spreaded error(a.ka Backpropagation). Basic problematic of processing the picture and resilence of network against degradation picture by a noise and compression JPEG is also described here. Second part is directed to practical realization of feed foward multiple-layer network with recognition the binary patterns of alphabetical letters and numbers 0 to 9, which was created in Matlab and Simulink environment. Next and final part is about practical realization of feed foward network with recognition the grayscale patterns of alphabetical letters and numbers 0 to 9, which was also created in Matlab and Simulink environment.
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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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Artificial neural network RCE
Maceček, Aleš ; Klusáček, Jan (referee) ; Jirsík, Václav (advisor)
This paper is focused on an artificial neural network RCE, especially describing the topology, properties and learning algorithm of the network. This paper describes program uTeachRCE developed for learning the RCE network and program RCEin3D, which is created to visualize the RCE network in 3D space. The RCE network is compared with a multilayer neural network with a learning algorithm backpropagation in the practical application of recognition letters. For a descriptions of the letters were chosen moments invariant to rotation, translation and scaling image.
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Analysis of Human Signature Based on Artificial Neural Network
Ševčík, Pavel ; Horák, Karel (referee) ; Pohl, Jan (advisor)
This bachelor thesis deals with methods of human signature and its analysis in practical service of artificial neural network. Actual processing and analysis of human signature consist in few steps. First of all, the signature pattern is digitized and processed with the assistance of preprocessing and segmentation methods. Afterwards, the object of human signature pattern is described with the assistance of centric geometric moments and moments invariant characteristics. Finally, the pattern is classified by multilayer perceptron, whose outputs determine the person, to that signature belongs to.
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