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Democratization in Europe between 1972 and 2002: Why it did not lead to war? Comparative case study based on fuzzy set method
Brázová, Věra - Karin ; Střítecký, Vít (advisor) ; Oberpfalzerová, Hana (referee)
The thesis focuses on European countries which underwent so-called partial democratization in the last quarter of the 20th century. It starts from the polemic with Mansfield and Snyder who claim that a (partial) democratization leads to war. The development in Europe of the last quarter of the 20th century, however, seems to contradict this notion. The aim of the thesis is, thus, to contribute to the debate of war-proneness of democratizing states by answering the following question: What caused that the democratization did not lead to war in many cases? Due to the nature of the research question as well as to the number of cases (i.e. 20) the method applied here is qualitative comparative analysis using the so-called fuzzy set method. The application of this method as such is a secondary aim of the thesis. Possible causal conditions of the absence of war which are under study here also derive mostly from the conclusions made by Mansfield and Snyder. The main focus is put on the so-called golden parachute. Among other causes are strong institutions - conceptualized here as weak and weakened executive, political integration into international community, duration of independent statehood and at least some experience with democracy - and developed economy - conceptualized through GDP, economic...
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Vágní informace na konečných abecedách a její monotónní charakteristiky
Kovářová, Lenka ; Beneš, Viktor (advisor) ; Kupsa, Michal (referee)
Title: Vague information on finite alphabets and its monotonous characteristics Author: Mgr. Lenka Kovářová Department: Department of Probability and Mathematical Statistics Supervisor: prof. RNDr. Viktor Beneš, DrSc. Abstract: The bachelor thesis is focused on information-theoretic source of messages with vague recognition from a final general alphabet. The aim of this work is to compile an overview of existing approaches to entropy and information. There were published several approaches how to convert to the fuzzy set theory the concept of entropy, which was originally introduced in physics, mathematically expressed as an additive-probability model and adjusted for Shannon probabilistic information source. Most of these approaches maintains the additive-probability model, while the emphasis in the theory of fuzzy sets is laid on the characteristics of minimum and maximum. Keywords: Entropy, Information, Fuzzy sets, Vague Entropy, Vague Information 1
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Hodnocení Výsledků Fuzzy Shlukování
Říhová, Elena ; Pecáková, Iva (advisor) ; Řezanková, Hana (referee) ; Žambochová, Marta (referee)
Cluster analysis is a multivariate statistical classification method, implying different methods and procedures. Clustering methods can be divided into hard and fuzzy; the latter one provides a more precise picture of the information by clustering objects than hard clustering. But in practice, the optimal number of clusters is not known a priori, and therefore it is necessary to determine the optimal number of clusters. To solve this problem, the validity indices help us. However, there are many different validity indices to choose from. One of the goals of this work is to create a structured overview of existing validity indices and techniques for evaluating fuzzy clustering results in order to find the optimal number of clusters. The main aim was to propose a new index for evaluating the fuzzy clustering results, especially in cases with a large number of clusters (defined as more than five). The newly designed coefficient is based on the degrees of membership and on the distance (Euclidean distance) between the objects, i.e. based on principles from both fuzzy and hard clustering. The suitability of selected validity indices was applied on real and generated data sets with known optimal number of clusters a priory. These data sets have different sizes, different numbers of variables, and different numbers of clusters. The aim of the current work is regarded as fulfilled. A key contribution of this work was a new coefficient (E), which is appropriate for evaluating situations with both large and small numbers of clusters. Because the new validity index is based on the principles of both fuzzy clustering and hard clustering, it is able to correctly determine the optimal number of clusters on both small and large data sets. A second contribution of this research was a structured overview of existing validity indices and techniques for evaluating the fuzzy clustering results.
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The Application of Fuzzy Logic for Evaluation of Quality of Customers
Šulc, Ondřej ; Vítek, Pavel (referee) ; Dostál, Petr (advisor)
This master thesis deals with the application of fuzzy logic for evaluating the quality of customers in company PPL CZ s.r.o. For evaluation was used Microsoft Excel and MathWorks MATLAB. There are going to be created models using fuzzy logic. The introductory part is devoted to the theoretical bases that are necessary to understand the whole issue. The second part includes information about the company and analyzes of the current situation. Finally, the last part presents fuzzy models used to evaluation of existing customers.
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Neural-Fuzzy Systems
Dalecký, Štěpán ; Samek, Jan (referee) ; Zbořil, František (advisor)
The thesis deals with artificial neural networks theory. Subsequently, fuzzy sets are being described and fuzzy logic is explained. The hybrid neuro-fuzzy system stemming from ANFIS system is designed on the basis of artificial neural networks, fuzzy sets and fuzzy logic. The upper-mentioned systems' functionality has been demonstrated on an inverted pendulum controlling problem. The three controllers have been designed for the controlling needs - the first one is on the basis of artificial neural networks, the second is a fuzzy one, and the third is based on ANFIS system. The thesis is aimed at comparing the described systems, which the controllers have been designed on the basis of, and evaluating the hybrid neuro-fuzzy system ANFIS contribution in comparison with particular theory solutions. Finally, some experiments with the systems are demonstrated and findings are assessed.
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Fuzzy Neural Networks
González, Marek ; Rozman, Jaroslav (referee) ; Zbořil, František (advisor)
This thesis focuses on fuzzy neural networks. The combination of the fuzzy logic and artificial neural networks leads to the development of more robust systems. These systems are used in various field of the research, such as artificial intelligence, machine learning and control theory. First, we provide a quick overview of underlying neural networks and fuzzy systems to explain fundamental ideas that form the basis of the fields, and follow with the introduction of the fuzzy neural network theory, classification and application. Then we describe a design and a realization of the fuzzy associative memory, as an example of these systems. Finally, we benchmark the realization using the pattern recognition and control tasks. The results are evaluated and compared against existing systems.
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