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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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Vývoj v oblasti elektronických systémů automobilů a jejich diagnostika
Šenk, Vladislav
The topic of this bachelor's thesis is the development in the field of electronic systems and their diagnostics. In the first part, the work is focused on ADAS assistance systems, where individual sensors used by assistance systems are described. Furthermore, their basic division is presented here, followed by a description of the individual assistance systems. The following section is focused on the occurrence of artificial intelligence in automobiles. Definitions, development and learning strategies are provided here. Diagnostic systems are presented in the third chapter. The chapter ends with the application of artificial intelligence as a diagnostic system. The next chapter describes the electrical installation and describes the types of wiring systems. The last chapter is devoted to autonomous systems, whether they are in use today or systems that have their own text summaries but the technology for them has not been developed. This chapter also covers environmental and driver monitoring and achieving minimal risk.
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Diagnostické systémy pro osobní automobily
Zukal, Jaromír
The Bachelor thesis is focused on diagnostic systems in passenger cars. The first part of the thesis contains theoretical partition of serial and parallel diagnostics. Next, the theses is concentrated on communication, systems in diagnostics and standards. In the last part of the thesis is description of measuring devices, measurement and conclusion.
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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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