Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Neuro-fuzzy rule generation: survey in soft computing framework
IEEE Transactions on Neural Networks
Formal concept analysis in knowledge discovery: a survey
ICCS'10 Proceedings of the 18th international conference on Conceptual structures: from information to intelligence
Applying the JBOS reduction method for relevant knowledge extraction
Expert Systems with Applications: An International Journal
Extracting reducible knowledge from ANN with JBOS and FCANN approaches
Expert Systems with Applications: An International Journal
Review: Formal Concept Analysis in knowledge processing: A survey on models and techniques
Expert Systems with Applications: An International Journal
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Nowadays, artificial neural networks (ANN) are being widely used in the representation of different systems and physics processes. In this paper, a neural representation of the cold rolling process will be considered. In general, once trained, the networks are capable of dealing with operational conditions not seen during the training process, keeping acceptable errors in their responses. However, humans cannot assimilate the knowledge kept by those networks, since such knowledge is implicit and difficult to be extracted. For this reason, the neural networks are considered a ''black-box''. In this work, the FCANN method based on formal concept analysis (FCA) is being used in order to extract and represent knowledge from previously trained ANN. The new FCANN approach permits to obtain a non-redundant canonical base with minimum implications, which qualitatively describes the process. The approach can be used to understand the relationship among the process parameters through implication rules in different operational conditions on the load-curve of the cold rolling process. Metrics for evaluation of the rules extraction process are also proposed, which permit a better analysis of the results obtained.