University of Pardubice
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for academic year 2016/2017
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Course: Modelling in Natural Sciences

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Course title Modelling in Natural Sciences
Course code FES/APMV
Organizational form of instruction Lecture
Level of course Doctoral
Year of study not specified
Semester Winter and summer
Number of ECTS credits 10
Language of instruction Czech
Status of course unspecified
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
Lecturer(s)
  • Musílek Kamil, doc. PharmDr. Ph.D.
Course content
Complex systems and cybernetic concepts. Soft and Hard systems. Nonlinear control systems, adaptive control systems, optimization options. Multi-criteria decision analysis. Decision making using the "Rule-Based Reasoning", "Case-Based Reasoning" and "Rough Sets Theory."

Learning activities and teaching methods
unspecified, Methods of individual activities
Learning outcomes
Computer modelling in natural sciences belongs to a discipline that deals with observation and explanation of natural processes by in silico methods. It describes bio-molecular interactions between live organisms and their components. Presented lectures include for example bio-informatics, bio-molecular electrostatics, molecular modelling and docking.
PhD students will be able to use the system approach for problem formulation, decomposition and formalization. They are able to design and analyze models of different classes of complex systems.
Prerequisites
unspecified

Assessment methods and criteria
Oral examination, Creative work analysis

Students will process a work independently within the scope of a research paper in this course. The topic of the paper is in relation to the specific topic of the dissertation thesis.
Recommended literature
  • Carlsson, Ch., Fulle, R. Fuzzy Reasoning in Decision Making and Optimization. Physica Verl., New York, 2002..
  • Doumpos, M., Grigoroudis, E. (eds.). Multicriteria Decision Aid and Artificial inteligence. Links, Theory and Applications. John Wiley and Sons, Inc., West Sussex, 2013..
  • Kou, Gang. Data processing for the AHP/ANP. Berlin: Springer-Verlag, 2013. ISBN 978-3-642-29212-5.
  • Pal, S. K., Shiu, S. C. K. Foundation of Soft Case-Based Reasoning. John Wiley and Sons, Inc., New Persey, 2004..
  • Saaty, T. L. The Analytic Hierarchy Process. New York : McGraw-Hill International Book Company, 1980. ISBN 0070543712.
  • Shinners, S. M. Modern Control System Theory and Design. NJ : John Wiley and Sons, 1998.
  • Slowinski R. (ed.). Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory. Kluwer Academic Publisher, Dordrecht, 1992..
  • Turban, Efraim. Decision support and business inteligence systems. Upper Saddle River: Pearson Prentice Hall, 2007. ISBN 0-13-198660-0.
  • Turban, Efraim. Decision support systems and intelligent systems. Upper Saddle River: Pearson Education, 2005. ISBN 0-13-046106-7.
  • Watson, Ian D. Applying case-based reasoning : techniques for enterprise systems. San Francisco: Morgan Kaufmann, 1997. ISBN 1-55860-462-6.


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester
University of Pardubice, date of update: 29.01.2026 23:50. Data created for academic year 2016/2017