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Lecturer(s)
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Olej Vladimír, prof. Ing. CSc.
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Hájek Petr, prof. Ing. Ph.D.
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Course content
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Fuzzy inference systems. The Mamdani fuzzy inference system design. The Takagi-Sugeno fuzzy inference system design. Neural networks key concepts. The division of object sets to training and testing sets. Neural networks models. The process of neural networks study. Designing models for economics processes classification and prediction using neural networks. Evolutionary stochastic optimising algorithms. Optimising using genetic algorithms. Neuro-fuzzy-genetic systems. Using artificial and computational intelligence in decision-support and management.
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Learning activities and teaching methods
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Monologic (reading, lecture, briefing), Dialogic (discussion, interview, brainstorming), Methods of individual activities
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Learning outcomes
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The aim of the course is to provide knowledge of the model design in the field of artificial and computer intelligence and of the possibilities of their application to different areas of social life, especially the economics, social and environmental spheres.
The aim of the course is to provide knowledge of the model design in the field of artificial and computer intelligence and of the possibilities of their application to different areas of social life, especially the economics, social and environmental spheres.
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Prerequisites
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unspecified
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Assessment methods and criteria
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Written examination, Work-related product analysis
Completion and successful defence of two projects with a success rate of at least 60 %, written tests throughout the semester. The assignment grade consists of 50 % project result and 50 % written test result. The examination is written.
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Recommended literature
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HAYKIN, S. S. Neural Networks: A Comprehensive Foundation. Prentice-Hall, 1999.
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KUNCHEVA, L. I. Fuzzy Classifier Design. A Springer Verlag Company, Germany, 2000. ISBN 80-903024-9.
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