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Neural Network Letter Recognition
Kluknavský, František ; Hradiš, Michal (referee) ; Šilhavá, Jana (advisor)
This work uses handwritten character recognition as a model problem for using multilayer perceptron, error backpropagation learning algorithm and finding their optimal parameters, hidden layer size, learning rate and length, ability to handle damaged data. Results were acquired by repeated simulation and testing the neural network using 52,152 English lowercase letters. Best results, smallest network and shortest learning time was at 60 neurons in the hidden layer and learning rate of 0.01. Bigger networks achieved the same ability to recognize unknown patterns and higher robustness at highly damaged data processing.
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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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Prediction of Raining from Meteoradar
Vlček, Michael ; Pešán, Jan (referee) ; Szőke, Igor (advisor)
This thesis deals with rain prediction using information from meteoradar images and some other relevant factors through the computational model of a neural network. It focuses on exploring different prediction possibilities using this model and defining the most successful model configuration to fulfill the chosen task.
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Image processing with neural networks
Gróf, Zoltán ; Pohl, Jan (referee) ; Jirsík, Václav (advisor)
This bachelor’s thesis centralizes on the possible uses of neural networks in the field of computer vision. This work contains basic theoretic knowledge of the field of neural networks and image processing. It discusses how successfully can neural networks be applied through the separate steps of image processing, what kind of neural networks are suitable for these steps, and what are the problems that might appear with their use. The work discusses the fields of classification and image understanding in a more detailed level. It’s shown how the use of neural networks can be appropriate in these applications. An own program was created as part of this work to demonstrate the classification capabilities of neural networks. It’s shown a neural network is created and trained for the recognition of handwritten numbers. The trained neural network was subject to different tests, through which the conclusion was reached, that it works with a high success rate, but is sensitive to changes in the input objects: change of size and location. A number of possible solutions were designed for this problem.
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Usage of the MATLAB environment for neural networks
Lenk, Peter ; Atassi, Hicham (referee) ; Škorpil, Vladislav (advisor)
This bachelor thesis discusses the basic theory and modelling of neural networks in the software environment of MATLAB. The thesis can be divided into four parts. After an introduction into the thesis, the theoretical background of the neural netwoks is explained in the first chapter. This chapter features a brief history and a biological background of neural networks and deals with the basic network architectures and the training processes. The next part is the description of how to implement networks in a general way using the MATLAB enviroment, so it deals with preparation of data, creation, simulation and training of a neural network. The last part of the paper covers a design of two excersises created in order to introduce modelling of the neural networks in the MATLAB enviroment to the students.
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The Use of Artificial Intelligence on Capital Markets
Dzuro, Daniel ; Budík, Jan (referee) ; Dostál, Petr (advisor)
The objective of this thesis is to evaluate the possibility of creating a tool capable of predicting commodity prices. Along with other business strategies, tools and markets analyses for financial and capital markets, this tool should help make the best estimate of future developments on the observed markets. The main market, on which this work is focused, is the agricultural commodities market, namely corn and its related markets. The fundamental basis upon which the arguments in this thesis are built, is the use of artificial intelligence, particularly neural networks. The whole application is presented using a graphical user interface that allows even those with little or no understanding of this field to delve deeper into the interesting area - using modern computer systems to support trading activities.
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Mathematical Modeling of Company Efficiency Using Neural Networks in Maple
Bartulec, Tomasz ; Vašátko, Jiří (referee) ; Chvátalová, Zuzana (advisor)
The goal of this thesis is to study the possibilities of Artificial neural network as an innovative mathematical methods for financial analysis of company performance, to find out what are today´s requests for performance evaluation of companies are and to identify possible ways how to use this relatively new concept in this area. When processing the possibilities of the computer program Maple for mathematical calculations will be applied. Intermediate objectives are then acquainted with the basic principle on which the artificial neural networks works, to analyze the financial performance of specific company and evaluate potential predictive abilities of the proposed network. The result of the work should be evaluating the success of this approach to financial analysis and evaluation of its use in practice.
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Convolutional neural network for image processing
Krajčovičová, Mária ; Rajmic, Pavel (referee) ; Burget, Radim (advisor)
Goal of this Diploma thesis was Convolutional neural network investigation in last years. Diploma thesis also contains information about designing of appropriate Convolutional neural network models and implementation of these models in Java programming language. Result of the thesis are comparison and evaluation of results which were reached from implemented application.
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Design of Switch Used in Modern Communication Networks
Mojžiš, Ľubomír ; Novotný, Bohumil (referee) ; Škorpil, Vladislav (advisor)
This diploma thesis deals with switching components of modern communication networks. A switch architecture focused on the quality of service is described in this paper and there are two switch models designed and simulated in the simulating program MATLAB-SIMULINK. The first model is based on classic switching and the other one is controlled by neural network. A laboratory exercise suitable for communication networks education is created in this paper on the basis of designed models.
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