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Optimization Algorithm with Probability Direction Vector
Pohl, Jan ; Pokorný, Miroslav (referee) ; Matoušek, Radomil (referee) ; Jirsík, Václav (advisor)
This disertation presents optimization algorithm with probability direction vector. This algorithm, in its basic form, belongs to category of stochastic optimization algorithms. It uses statistically effected perturbation of individual through state space. This work also represents modification of basic idea to the form of swarm optimization algoritm. This approach contains form of stochastic cooperation. This is one of the new ideas of this algorithm. Population of individuals cooperates only through modification of probability direction vector and not directly. Statistical tests are used to compare resultes of designed algorithms with commonly used algorithms Simulated Annealing and SOMA. This part of disertation also presents experimental data from other optimization problems. Disertation ends with chapter which seeks optimal set of control variables for each designed algorithm.
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Automated Weight Tuning for Rule-Based Knowledge Bases
Valenta, Jan ; Pokorný, Miroslav (referee) ; Zbořil, František (referee) ; Jirsík, Václav (advisor)
This dissertation thesis introduces new methods of automated knowledge-base creation and tuning in information and expert systems. The thesis is divided in the two following parts. The first part is focused on the legacy expert system NPS32 developed at the Faculty of Electrical Engineering and Communication, Brno University of Technology. The mathematical base of the system is expression of the rule uncertainty using two values. Thus, it extends information capability of the knowledge-base by values of the absence of the information and conflict in the knowledge-base. The expert system has been supplemented by a learning algorithm. The learning algorithm sets weights of the rules in the knowledge base using differential evolution algorithm. It uses patterns acquired from an expert. The learning algorithm is only one-layer knowledge-bases limited. The thesis shows a formal proof that the mathematical base of the NPS32 expert system can not be used for gradient tuning of the weights in the multilayer knowledge-bases. The second part is focused on multilayer knowledge-base learning algorithm. The knowledge-base is based on a specific model of the rule with uncertainty factors. Uncertainty factors of the rule represents information impact ratio. Using a learning algorithm adjusting weights of every single rule in the knowledge base structure, the modified back propagation algorithm is used. The back propagation algorithm is modified for the given knowledge-base structure and rule model. For the purpose of testing and verifying the learning algorithm for knowledge-base tuning, the expert system RESLA has been developed in C#. With this expert system, the knowledge-base from medicine field, was created. The aim of this knowledge base is verify learning ability for complex knowledge-bases. The knowledge base represents heart malfunction diagnostic base on the acquired ECG (electrocardiogram) parameters. For the purpose of the comparison with already existing knowledge-basis, created by the expert and knowledge engineer, the expert system was compared with professionally designed knowledge-base from the field of agriculture. The knowledge-base represents system for suitable cultivar of winter wheat planting decision support. The presented algorithms speed up knowledge-base creation while keeping all advantages, which arise from using rules. Contrary to the existing solution based on neural network, the presented algorithms for knowledge-base weights tuning are faster and more simple, because it does not need rule extraction from another type of the knowledge representation.
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Czechoslovak-Mongolian political, economical and cultural relations 1968-1984
Pokorný, Miroslav ; Mikeska, Tomáš (advisor) ; Koura, Petr (referee)
This thesis research relations between Czechoslovakia and the Mongolian People's Republic during the normalization process. The work is focused on the transformation of relations between both countries from the Prague Spring period to the revocation of Yumjaagiin Tsedenbal in 1984. The remaining five chapters, covering the time scope of foreign policy, monitor economical relationships, ideological and cultural section of Czechoslovak-Mongolian relations during the normalization process. The content of the thesis draws chiefly primary sources: government delegations, period articles, official reports, cultural campaigns and other archival material. Thanks to these sources it was possible to authentically describe relations of both countries
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Czechoslovak-Mongolian political, economical and cultural relations 1968-1984
Pokorný, Miroslav ; Mikeska, Tomáš (advisor) ; Koura, Petr (referee)
This thesis research relations between Czechoslovakia and the Mongolian People's Republic during the normalization process. The work is focused on the transformation of relations between both countries from the Prague Spring period to the revocation of Yumjaagiin Tsedenbal in 1984. The remaining five chapters, covering the time scope of foreign policy, monitor economical relationships, ideological and cultural section of Czechoslovak-Mongolian relations during the normalization process. The content of the thesis draws chiefly primary sources: government delegations, period articles, official reports, cultural campaigns and other archival material. Thanks to these sources it was possible to authentically describe relations of both countries
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Optimization Algorithm with Probability Direction Vector
Pohl, Jan ; Pokorný, Miroslav (referee) ; Matoušek, Radomil (referee) ; Jirsík, Václav (advisor)
This disertation presents optimization algorithm with probability direction vector. This algorithm, in its basic form, belongs to category of stochastic optimization algorithms. It uses statistically effected perturbation of individual through state space. This work also represents modification of basic idea to the form of swarm optimization algoritm. This approach contains form of stochastic cooperation. This is one of the new ideas of this algorithm. Population of individuals cooperates only through modification of probability direction vector and not directly. Statistical tests are used to compare resultes of designed algorithms with commonly used algorithms Simulated Annealing and SOMA. This part of disertation also presents experimental data from other optimization problems. Disertation ends with chapter which seeks optimal set of control variables for each designed algorithm.
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Automated Weight Tuning for Rule-Based Knowledge Bases
Valenta, Jan ; Pokorný, Miroslav (referee) ; Zbořil, František (referee) ; Jirsík, Václav (advisor)
This dissertation thesis introduces new methods of automated knowledge-base creation and tuning in information and expert systems. The thesis is divided in the two following parts. The first part is focused on the legacy expert system NPS32 developed at the Faculty of Electrical Engineering and Communication, Brno University of Technology. The mathematical base of the system is expression of the rule uncertainty using two values. Thus, it extends information capability of the knowledge-base by values of the absence of the information and conflict in the knowledge-base. The expert system has been supplemented by a learning algorithm. The learning algorithm sets weights of the rules in the knowledge base using differential evolution algorithm. It uses patterns acquired from an expert. The learning algorithm is only one-layer knowledge-bases limited. The thesis shows a formal proof that the mathematical base of the NPS32 expert system can not be used for gradient tuning of the weights in the multilayer knowledge-bases. The second part is focused on multilayer knowledge-base learning algorithm. The knowledge-base is based on a specific model of the rule with uncertainty factors. Uncertainty factors of the rule represents information impact ratio. Using a learning algorithm adjusting weights of every single rule in the knowledge base structure, the modified back propagation algorithm is used. The back propagation algorithm is modified for the given knowledge-base structure and rule model. For the purpose of testing and verifying the learning algorithm for knowledge-base tuning, the expert system RESLA has been developed in C#. With this expert system, the knowledge-base from medicine field, was created. The aim of this knowledge base is verify learning ability for complex knowledge-bases. The knowledge base represents heart malfunction diagnostic base on the acquired ECG (electrocardiogram) parameters. For the purpose of the comparison with already existing knowledge-basis, created by the expert and knowledge engineer, the expert system was compared with professionally designed knowledge-base from the field of agriculture. The knowledge-base represents system for suitable cultivar of winter wheat planting decision support. The presented algorithms speed up knowledge-base creation while keeping all advantages, which arise from using rules. Contrary to the existing solution based on neural network, the presented algorithms for knowledge-base weights tuning are faster and more simple, because it does not need rule extraction from another type of the knowledge representation.
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Podnikatelský záměr - založení reklamní agentury
Vyčichlová, Soňa ; Srpová, Jitka (advisor) ; Pokorný, Miroslav (referee)
Podnikatelský plán je vypracován pro nově vznikající reklamní agenturu, kterou se zakladatelé rozhodli založit jako sdružení fyzických osob bez právní subjektivity s výhledem na transformaci na společnost s ručením omezeným do 1-2 let. Agentura bude používat obchodní jméno GOOD ONE. Základní pilíře úspěchu spatřuje v lidském potenciálu všeobecně a ve zkušenostech zakladatelů získaných předchozím působením v reklamní oboru. Hlavní specializací agentury budou podlinkové aktivity, produkci a incentivní turistiku. Plánovaný obchodní plán a na něj navazující finanční výkazy prokazují reálnost tohoto podnikatelského záměru a dokládají kladný hospodářský výsledek.
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