Automation and Information Engineering in Chemistry and Food Industry – master (full-time, attendance method), 4. semester
Level of study:
Prerequisites for registration:
Students individually elaborate 3 projects within the semester. They submit them electronically. They have to present the results and defend them. The students are rated in accordance with the study rules of the STU.
Learning outcomes of the course unit:
Students know to apply artificial intelligence methods (methods of patterns recognition, problem solving, expert systems, fuzzy logic, fuzzy modeling and control, artificial neural networks, evolutionary algorithms) to solve problems in the identification, modeling and control of technological processes.
Introduction to problems of artificial intelligence
Recognition methods (statistical and structural)
Expert Systems (diagnostic and planning)
Fuzzy logic, fuzzy identification, fuzzy modelling and control
Neural networks in identification and management
Neuro - fuzzy control
Evolutionary algorithms in intelligent control
Recommended or required reading:
NÁVRAT, P. Umelá inteligencia. Bratislava: STU, 2002.
NÁVRAT, P. – BIELIKOVÁ, M. – BEŇUŠKOVÁ, Ľ. – KAPUSTÍK, I. – UNGER, M. Umelá inteligencia. Bratislava : STU v Bratislave, 2007. 393 p. ISBN 978-80-227-2629-0.
WARWICK, K. Neural networks for control and systems. London : Peter Peregrinus, 1992. 260 p. ISBN 0-86341-279-3.
TSOUKALAS, L H. – UHRING, R E. Fuzzy and neural approaches in engineering. New York : John Wiley & Sons, 1997. 587 p. ISBN 0-471-16003-2.
ZHANG, H. – LIU, D. Fuzzy Modeling and Fuzzy Control. Boston : Birkhäuser, 2006. 416 p. ISBN 978-0-8176-4491-8.
Institute of Information Engineering, Automation and Mathematics was established in 1.1.2006 from two departments: Department of Information Engineering and Process Control and Department of Mathematics.