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Artificial neural networks for clustering and rule extraction
Iša, Jiří ; Mrázová, Iveta (advisor) ; Jiroutek, Pavel (referee)
Rule extraction with neural networks has been a common research topic over the last decades. This master thesis proposes a novel growing fuzzy inference neural network, based on the principle of growing neural structures [5]. This allows the network to adjust iteratively its number of hidden neurons. For the purpose of this network an existing clustering algorithm is enhanced to improve the sensitivity to the requested output. A novel fast weights adaptation, inspired by the fuzzy set theory, is also suggested. The characteristics of the proposed model and a new method of the selection of significant input features support the induction of a relatively small amount of simple fuzzy rules. The introduced techniques have been experimentally tested on real-world data describing the relationship between various types of housing in the Boston area and its price. The data was obtained from the "Boston housing" dataset.
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Decision Trees and Knowledge Extraction
Vitinger, Jiří ; Mrázová, Iveta (advisor) ; Jiroutek, Pavel (referee)
The goal of data mining is to extract knowledge, dependencies and rules from data sets. Many complex methods were developed to solve it. This thesis presents some of the most important methods, which include the decision trees with algorithms ID3, C4.5 and CART, neural networks like multilayer neural networks with the backpropagation algorithm, RBF networks, Kohonens maps and some modifications of LVQ method. There are also described some clustering methods like hierarchical clustering, QT clustering, kmeans method and its fuzzy modification. The work also includes data pre-processing techniques, which are very important in order to obtain better results of data mining process. Experimental part of the work compares the presented methods by means of the results of many tests on real-world data sets. The results can be used as a guide to choose an appropriate method and its parameters for some given data set. In this work there is presented author's implementation of the decision trees C4.5 and CART in C#. In the application it is possible to watch details of algorithms work. The application provides an API enabling an implementation of new algorithms.
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Algorithms for spectral analysis of stars
Hudec, Lukáš ; Mráz, František (advisor) ; Jiroutek, Pavel (referee)
THE MAIN OUTPUT OF THIS WORK IS A COMPUTER SOFTWARE FOR AUTOMATICAL PROCESSING OF SCANNED ASTRONOMICAL PLATES, WHICH COMES FROM EXTENSIVE ARCHIVE FROM GERMANY ASTRONOMICAL OBSERVATION IN SONNEBERG. APPLICATION IS GOING THROUGH THE CONTENT OF THE PLATE DETECTING ALL ASTRONOMICAL OBJECTS WITH PARAMETERS, WHICH ARE DETERMINATED BY USER. FOR CHOSEN OBJECTS IS THEN EXECUTED SPECTRAL ANALYSE AND CONTINGENT STAR CLASIFICATION ACCORDING TO ASTRONOMICAL CATALOGUE OF SPECTRES.
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A comparison between natural and artificial learning
Jiroutek, Pavel
This paper deals with design and implementation of an evolutionary system for control of an autonomous mobile robot. This system should make possible an adaptation to a group of tasks, that can be similarly defined for a living being. We use results of real experiments with laboratory rats and tasks, which these rats are able to learn. The robot control system is a combination of several methods of mobile robotics and artificial intelligence. The adaptable part of the control system is based on genetic algorithms and neural networks. This work covers a wide range of problems related with this subject - various elements of the control system, the robot control and implementation, and also components of the test environment, which can be used to execute evolutionary experiments with real robots. The final part of this work includes results of our practical experiments with the robot and their comparison with real testing of rats on similar tasks.
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A comparison between natural and artificial learning
Jiroutek, Pavel
This paper deals with design and implementation of an evolutionary system for control of an autonomous mobile robot. This system should make possible an adaptation to a group of tasks, that can be similarly defined for a living being. We use results of real experiments with laboratory rats and tasks, which these rats are able to learn. The robot control system is a combination of several methods of mobile robotics and artificial intelligence. The adaptable part of the control system is based on genetic algorithms and neural networks. This work covers a wide range of problems related with this subject - various elements of the control system, the robot control and implementation, and also components of the test environment, which can be used to execute evolutionary experiments with real robots. The final part of this work includes results of our practical experiments with the robot and their comparison with real testing of rats on similar tasks.
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Algorithms for spectral analysis of stars
Hudec, Lukáš ; Jiroutek, Pavel (referee) ; Mráz, František (advisor)
THE MAIN OUTPUT OF THIS WORK IS A COMPUTER SOFTWARE FOR AUTOMATICAL PROCESSING OF SCANNED ASTRONOMICAL PLATES, WHICH COMES FROM EXTENSIVE ARCHIVE FROM GERMANY ASTRONOMICAL OBSERVATION IN SONNEBERG. APPLICATION IS GOING THROUGH THE CONTENT OF THE PLATE DETECTING ALL ASTRONOMICAL OBJECTS WITH PARAMETERS, WHICH ARE DETERMINATED BY USER. FOR CHOSEN OBJECTS IS THEN EXECUTED SPECTRAL ANALYSE AND CONTINGENT STAR CLASIFICATION ACCORDING TO ASTRONOMICAL CATALOGUE OF SPECTRES.
|
|
|
Artificial neural networks for clustering and rule extraction
Iša, Jiří ; Jiroutek, Pavel (referee) ; Mrázová, Iveta (advisor)
Rule extraction with neural networks has been a common research topic over the last decades. This master thesis proposes a novel growing fuzzy inference neural network, based on the principle of growing neural structures [5]. This allows the network to adjust iteratively its number of hidden neurons. For the purpose of this network an existing clustering algorithm is enhanced to improve the sensitivity to the requested output. A novel fast weights adaptation, inspired by the fuzzy set theory, is also suggested. The characteristics of the proposed model and a new method of the selection of significant input features support the induction of a relatively small amount of simple fuzzy rules. The introduced techniques have been experimentally tested on real-world data describing the relationship between various types of housing in the Boston area and its price. The data was obtained from the "Boston housing" dataset.
|
|
|
Decision Trees and Knowledge Extraction
Vitinger, Jiří ; Mrázová, Iveta (advisor) ; Jiroutek, Pavel (referee)
The goal of data mining is to extract knowledge, dependencies and rules from data sets. Many complex methods were developed to solve it. This thesis presents some of the most important methods, which include the decision trees with algorithms ID3, C4.5 and CART, neural networks like multilayer neural networks with the backpropagation algorithm, RBF networks, Kohonens maps and some modifications of LVQ method. There are also described some clustering methods like hierarchical clustering, QT clustering, kmeans method and its fuzzy modification. The work also includes data pre-processing techniques, which are very important in order to obtain better results of data mining process. Experimental part of the work compares the presented methods by means of the results of many tests on real-world data sets. The results can be used as a guide to choose an appropriate method and its parameters for some given data set. In this work there is presented author's implementation of the decision trees C4.5 and CART in C#. In the application it is possible to watch details of algorithms work. The application provides an API enabling an implementation of new algorithms.
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