This thesis deal with improvement behavior of temperature control in bioreactor. It was created model of system. Basic PI controller could solve all problems. After analysis of system were decided to used fuzzy controller. Fuzzy controller in comparison with classic PI controller has better behavior in this application.

This Master’s thesis analyses the problems connected with the selection of suitable supplier. It includes the proposal of the method of rating of suppliers. It can ensure the selection of optimal supplier for the firm.

Fuzzy logic is a complex tool for support for decision-making in various arrays of use. This thesis should analyze existing solutions in selected decision processes in companies and to bring more effective ways of decision-making using fuzzy logic in consequence.

This master’s thesis deals with design of models which allows to evaluate different plastic windows suppliers. Suggested model uses fuzzy logic to evaluate suppliers and is used as a support for decision-making. With small modifications, the model can be also used repeatedly. The solution itself is designed by MATLAB and MS Excel programs.

The thesis employs modern analysing and simulating methods. The thesis specifically deals with the practical use of fuzzy logic for selecting clients. The aim is to create a suitable system for listing submitted applications in a chronological order within a contributory company.

This thesis deals with the application of Markov chains for the production of concrete products. The theoretical part is focused on clarifying the concepts of risk management and describes the procedures for dealing with classical Markov chains. There are presented basics of fuzzy logic and finally there is explained the procedure using fuzzy logic in calculating of classical Markov chains in the subsection entitled Fuzzy Markov chains. The practical part describes production process, namely concrete pavements. On this production process is applied knowledge from the theoretical part and there is a comparison and evaluation of two methods of Marcov chains calculation (classic and fuzzy approach).

No / Extensive research has been performed for developing knowledge based intelligent monitoring systems for improving the reliability of manufacturing processes. Due to the high expense of obtaining knowledge from human experts, it is expected to develop new techniques to obtain the knowledge automatically from the collected data using data mining techniques. Inductive learning has become one of the widely used data mining methods for generating decision rules from data. In order to deal with the noise or uncertainties existing in the data collected in industrial processes and systems, this paper presents a new method using fuzzy logic techniques to improve the performance of the classical inductive learning approach. The proposed approach, in contrast to classical inductive learning method using hard cut point to discretize the continuous-valued attributes, uses soft discretization to enable the systems have less sensitivity to the uncertainties and noise. The effectiveness of the proposed approach has been illustrated in an application of monitoring the machining conditions in uncertain environment. Experimental results show that this new fuzzy inductive learning method gives improved accuracy compared with using classical inductive learning techniques.

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