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Methods of knowledge representation
Adamec, Jan ; Valenta, Jan (referee) ; Jirsík, Václav (advisor)
Thesis analyses the problems of energy sources evaluation during the reconstruction of an object or new building.There are described representatives of heat sources for given energy sources in text. rough view of how to proceed in searching for acceptable energy source is made here. this problematics is solved by the help of diagnostic expert system NPS32. created knowledge base could be used for sale support or for users for easier orientation in this problematics.
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Image Compression Based on Artificial Neural Network
Vondráček, Jiří ; Pohl, Jan (referee) ; Jirsík, Václav (advisor)
The thesis is focused on the image compression based on artificial neural network with practical implementation. The objective of this thesis is to explore possibilities of an image compression by artificial neural network and analyze results. In the theoretical part of the work, the fundamentals of artificial neural network are described and basic image compression techniques are explained. In the practical part there is a brief description of the compression program, the comparison of different settings and result evaluation.
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Knowledge representation methods
Verbík, Josef ; Polách, Petr (referee) ; Jirsík, Václav (advisor)
This thesis deals with methods of knowledge representation within expert systems. The thesis describes the expert systems and their history. Furthermore, it provides a description of individual types of expert systems and their components. Following this description, there is a characteristic of the knowledge representation. These characteristic has been divided into two parts. The first part describes the requirements neccessary for efficient knowledge representation. The second part describes the following individual methods of knowledge representation: rules, frames, semantic nets and predicate logic.
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Forex automated trading system based on neural networks
Kačer, Petr ; Honzík, Petr (referee) ; Jirsík, Václav (advisor)
Main goal of this thesis is to create forex automated trading system with possibility to add trading strategies as modules and implementation of trading strategy module based on neural networks. Created trading system is composed of client part for MetaTrader 4 trading platform and server GUI application. Trading strategy modules are implemented as dynamic libraries. Proposed trading strategy uses multilayer neural networks for prediction of direction of 45 minute moving average of close prices in one hour time horizon. Neural networks were able to find relationship between inputs and output and predict drop or growth with success rate higher than 50%. In live demo trading, strategy displayed itself as profitable for currency pair EUR/USD, but it was losing for currency pair GBP/USD. In tests with historical data from year 2014, strategy was profitable for currency pair EUR/USD in case of trading in direction of long-term trend. In case of trading against direction of trend for pair EUR/USD and in case of trading in direction and against direction of trend for pair GBP/USD, strategy was losing.
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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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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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The decision boundary
Gróf, Zoltán ; Hynčica, Tomáš (referee) ; Jirsík, Václav (advisor)
The main aim of this master's thesis is to describe the subject of the implementation of decision boundaries with the help of artificial neural networks. The objective is to present theoretical knowledge concerning this field and on practical examples prove these statements. The work contains basic theoretical description of the field of pattern recognition and the field of feature based representation of objects. A classificator working on the basis of Bayes decision is presented in this part, and other types of classificators are named as well. The work then deals with artificial neural networks in more detail; it contains a theoretical description of their function and their abilities in the creation of decision boundaries in the feature plane. Examples are shown from literature for the use of neural networks in corresponding problems. As part of this work, the program ANN-DeBC was created using Matlab, for the generation of practical results about the usage of feed-forward neural networks for the implementation of decision boundaries. The work contains a detailed description of this program, and the achieved results are presented and analyzed. It is shown as well, how artificial neural networks are creating decision boundaries in the form of geometrical shapes. The effects of the chosen topology of the neural network and the number of training samples on the success of the classification are observed, and the minimal values of these parameters are determined for the successful creation of decision boundaries at the individual examples. Furthermore, it's presented how the neural networks behave at the classification of realistically distributed training samples, and what methods can affect the shape of the created decision boundaries.
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Intelligent document
Šprta, Vlastimil ; Holek, Radovan (referee) ; Jirsík, Václav (advisor)
This diploma thesis, interested in intelligent documents, covers, in its introductory chapters, the basic problematics of inteligent documents. The major part of major part of these theorethical chapters is devoted to the analysis of possible practical applications of certain parts of knowledge management. The second part of the thesis, focused more on a practical usage of intelligent documents, includes the proposition of the actual structure and the realization of the specific intelligent document. This part also includes the evaluation and the examples of the differences between an ordinary electronic document and an intelligent document. The conclusion of the practical part of the thesis is the summary of all the findings concerning the practical implementation of an intelligent document and the evaluation of possible applications, extension possibilities and changes of such intelligent document.
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Kohonen self-organizing map
Žáček, Viktor ; Hynčica, Tomáš (referee) ; Jirsík, Václav (advisor)
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.
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E-learning study modules
Kosík, Tomáš ; Honzík, Petr (referee) ; Jirsík, Václav (advisor)
This master’s thesis is focused on description of e-learning by electronic form of teaching as a way of modern education. In theoretical part various forms, possibilities and basic structures of electronic education systems are described. It presents thorough analyses of positives and negatives of e-learning, both, from a technical perspective as well as from social point of view. In the second part of this thesis, the reader becomes familiar with e-learning system AI Tools used by UAMT FEEC VUT in Brno to support the teaching of basics of artificial intelligence. Furthermore, the work deals with the creation of three plug-in modules for this computer programme. These modules are programmed in C# and pursue matters of informed and uninformed state space search, and issues of forward and backward chaining used in expert systems. In conclusion, the results of theoretical and practical work are evaluated
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