Key points are not available for this paper at this time.
Artificial neural networks are efficient computing models which have shown their strengths in solving hard problems in artificial intelligence. They have also been shown to be universal approximators. Notwithstanding, one of the major criticisms is their being black boxes, since no satisfactory explanation of their behavior has been offered. In this paper, we provide such an interpretation of neural networks so that they will no longer be seen as black boxes. This is stated after establishing the equality between a certain class of neural nets and fuzzy rule-based systems. This interpretation is built with fuzzy rules using a new fuzzy logic operator which is defined after introducing the concept of f-duality. In addition, this interpretation offers an automated knowledge acquisition procedure.
Building similarity graph...
Analyzing shared references across papers
Loading...
José M. Benítez
Juan Luis Castro
Ignacio Requena
IEEE Transactions on Neural Networks
Universidad de Granada
Building similarity graph...
Analyzing shared references across papers
Loading...
Benítez et al. (Mon,) studied this question.
www.synapsesocial.com/papers/6a0f0d81b7cc3b883f230515 — DOI: https://doi.org/10.1109/72.623216
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: