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Granular Computing

Code 14497
Year 1
Semester S1
ECTS Credits 6
Workload OT(15H)
Scientific area Informatics
Entry requirements None.
Mode of delivery Face-to-face
Learning outcomes Granular computing offers a framework of theories, methodologies and tools that allows the use of information granules in the process of problem solving. This course offers a broad introduction to Granular Computing placing the emphasis on its applicability to real world problems. Students should be able to: - Formulate problems in granular computing. - Assess the strengths and weaknesses of several approaches to granular computing. - Assess and understand the key commonalities, synergies and differences in various granular computing approaches. - Apply granular computing techniques to problems such as data mining, automatic control, and image processing.
Syllabus 1. Foundations and methodology of Granular Computing. 2. Interval analysis. 3. Fuzzy sets. 4. Rough sets. 5. Hybrid Methods. 6. Applications and case studies. 7. Current trends of investigation.
Main Bibliography The main references are selected from a set of papers about current research (foundational and applicational) on the field of Granular Computing. Handbook of Granular Computing. Witold Pedrycz, Andrzej Skowron, and Vladik Kreinovich, editors. Wiley, July 2008.? Novel developments in granular computing: applications for advanced human reasoning and soft computation. JingTao Yao, editor. IGI Global, 2010.?
Language Portuguese. Tutorial support is available in English.
Last updated on: 2021-06-24

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