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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.

Master´s thesis deals about problematic of fuzzy logic, fuzzy sets and operation with them. Also thesis describing procedure of quantification risks through fuzzy sets, process of fuzzyfication, defuzzyfication a theirs application in the expert systems FMEA and FMECA. Using selected chapters of czech states standards are established critical areas in building process, it is a chapters about quality, safety and environment. With these areas by using fuzzy sets theory are created the procentual input values in the decision program, created by Matlab. Output from program is decision how big the risk rate is and if is necessarily make corrective measures. A concrete example is the production of concrete goods, describing production process and composition of fresh concrete. A practical example, created by using information from real company, is a decision about the risk rate in the qualitative area.