NDLI: FCANN: An Approach to Knowledge Representation From ANN Through Formal Concept Analysis

Content Provider	IEEE Xplore Digital Library
Author	Zarate, L.E. Dias, S.M.
Copyright Year	2006
Description	Author affiliation: Pontifical Catholic Univ. of Minas Gerais, Belo Horizonte (Zarate, L.E.; Dias, S.M.)
Abstract	Nowadays, artificial neural networks (ANN) are been widely used in the representation of physical process. Once trained, the nets are capable to solve unprecedented situations, keeping tolerable errors in their outputs. However, humans cannot assimilate the knowledge kept by those networks, since such knowledge is implicitly represented by their connection weights. Formal concept analysis (FCA) can be used in order to facilitate the extraction, representation and understanding of rules described by ANN. In this work, the approach FCANN to extract rules via FCA is applied to the cold rolling process. The approach has a sequence of steps as the use of a synthetic database where the data number variation per parameter is an adjustment factor to obtain more representative rules. The approach can be used to understand the relationship among the process parameters through implication rules
Starting Page	3773
Ending Page	3778
File Size	251914
Page Count	6
File Format	PDF
ISBN	1424403901
ISSN	1553572X
DOI	10.1109/IECON.2006.347262
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-11-06
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Knowledge representation Artificial neural networks Data mining Neural networks Artificial intelligence Humans Databases Algorithm design and analysis Shape Hybrid intelligent systems
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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