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Granular Computing

Code 11510
Year 1
Semester S1
ECTS Credits 6
Workload OT(15H)
Scientific area Informatics
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. Fundamentals 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 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.
Scientific papers selected by the teacher.
Planned learning activities and teaching methods Oral discussion of the main topics in class. Students will implement some of the studied techniques and analyze the results in concrete (real world) applications. Students will present some of the latest achievements in the field of Granular Computing.
Metodologias de Ensino e Critérios de Avaliação Avaliação de conhecimentos (8 pontos - 40%) - 1 teste
Relatórios de procedimentos práticos (8 pontos - 40%)
Avaliação das capacidades de discussão (4 pontos - 20%)

Presença obrigatória em 85% das aulas.
Language Portuguese. Tutorial support is available in English.
Last updated on: 2014-08-07

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