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Lecturer(s)
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Linda Bohdan, doc. RNDr. CSc.
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Čapek Jan, prof. Ing. CSc.
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Course content
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Artificial and computational intelligence. Synthesis and analysis of decision-making processes with uncertainty. Classification and prediction economic processes by fuzzy inference systems. Fuzzy inference system Mamdani. Fuzzy inference system Takagi-Sugeno. Models of neural networks, classification and prediction. Process learning in neural networks. Evolution stochastic optimization algorithms. Neuro-fuzzy-genetic systems. Computational intelligence in decision-making, control, classification and prediction. Ambient intelligence.
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Learning activities and teaching methods
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Methods of individual activities
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Learning outcomes
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The students should be able to design fuzzy inference systems for classification and prediction, especially in the economics, social and environmental spheres, as well as to design models based on neural and fuzzy neural networks.
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Prerequisites
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unspecified
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Assessment methods and criteria
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Discussion
Completion and successful defense of project from the field of dissertation work.
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Recommended literature
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GHOSH A., TSUTSUI S. Advances in Evolutionary Computing. Theory and Applications.. A Springer-Verlag Company, Germany, 2003.
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KUNCHEVA L. I. Fuzzy Classifier Design.. A Springer Verlag Company, Germany, 2000.
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KVASNIČKA V. a kol. Evolučné algoritmy.. STU, Bratislava, 2000.
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Kvasnička V. a kol. Úvod do teórie neurónových sietí. 1997, IRIS Bratislava.. IRIS, Bratislava, 1997.
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OLEJ V. Modelovanie ekonomických procesov na báze výpočtovej inteligencie.. Miloš Vognar - M&V, Hradec Králové, 2003. ISBN 80-903024-9-1.
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RUSSEL, S.-NORVIG, P. Artificial Intelligence. A Modern Approach. Prentice Hall. New Jersey, 2003.
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RUTKOWSKI L., KACPRZYK J. Advances in Soft Computing. Neural Networks and Soft Computing.. A Springer-Verlag Company, Germany, 2003.
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