This thesis discusses combinatorial optimization problems, its characteristics and solving methods. Different types of such problems are presented here and I hint at solution using classical heuristical algorithms. In the next part, I focus on artificial neural networks, their description and classification. In the last part, I'm comparing two neural network approaches for solving a travelling salesman problem on several examples.

The aim of this work is to implement a conversion from the scanned math formula to the editable form as a TEX file as an extension of the working OCR system. In this work we closely analyze this problem, its division into several smaller parts, such as math symbol recognition and a recognition of structure of math formulas, and their solutions together with a description of various solutions. We test our implementations using our database of symbols and math formulas. An important part of the work is also a creation of a set of complex applications with a sophisticated graphical user interface, which allow easy accommodation of conversion to the user's needs. During the conversion we work with images, which may contain insignificant noise caused by a scanner of lower quality.

The topic of the present thesis is an analysis of a sample data archive containing measured values of real and reactive power. The measurement in question took place in late 2006 and early 2007 using MEg40 recording measurement devices disposed in a station for transforming high voltage to low voltage in the Pražská energetika distribution network. The procedure of processing measured values, the preparation thereof for a subsequent processing by a neural network, and a final statistical evaluation of determined individual clusters -- typical daily take-off diagrams -- will be described. The results of the present thesis may be applied in the making of predictions of electrical energy consumption at a particular transformer station.

This master thesis present a method for building topological maps for mobile robot navigation using neural network and neural fuzzy network. The master thesis concentrates on classification method. Neural fuzzy network is compared with two neural networks. It was also designed control algorithm exploration environment for autonomous mobile robot. This will rereduce the time to build the map. I developed simulation program in Matlab, which simulate move mobile robot in unknown environment.

Work deal about self-organizing maps, especially about Kohonen self-organizing map. About creating of aplication, which realize creating and learning of self-organizing map. And about usage of self-organizing map for self-localization of robot.

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.

This paper deals with application of neural networks in image segmentation. First part is an introduction to image processing and neural networks, second part describes an implementation of segmentation system and presents results of experiments. The segmentation system enables to use different types of classifiers, various image features extraction and also to evaluate the success of segmentation. Two classifiers were created - a neural network (self-organizing map) and an algorithm K-means. Colour (RGB and HSV) and texture features and their combinations were used for classification. Texture features were extracted using a set of Gabor filters. Experiments with designed classifiers and feature extractors were carried out and results were compared.

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.

The diploma thesis deal with finding and testing neural networks, whose characteristics and parameters suitable for the active management of network element. Solves optimization task priority switching of data units from input to output. Work is focused largely on the use of Hopfield and Kohonen networks and their optimization. Result of this work are two models. The first theory is solved in Matlab, where each comparing the theoretical results of neural networks. The second model is a realistic model of the active element designed in Simulink

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.