FCANN: An Approach to Knowledge Representation From ANN Through Formal Concept Analysis - Application in the Cold Rolling Process

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 su...

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Bibliographic Details
Published inIECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics pp. 3773 - 3778
Main Authors Zarate, L.E., Dias, S.M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2006
Subjects
Algorithm design and analysis
Artificial intelligence
Artificial neural networks
Data mining
Humans
Hybrid intelligent systems
Knowledge representation
Neural networks
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Summary: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
ISSN:1553-572X
DOI:10.1109/IECON.2006.347262