Rule extraction as a formal method for the verification and validation of neural networks

By Brian J. Taylor, Marjorie Darrah. 2005

37 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Tameru Hailesilassie
2. Thangairulappan Kathirvalavakumar
3. Gethsiyal Augasta M
4. Octavio Loyola-González
5. Gesina Schwalbe
6. Bettina Finzel
7. Marco F. Huber
8. Nadia Burkart

In Control and Systems Engineering

1. Tameru Hailesilassie
2. Thangairulappan Kathirvalavakumar
3. Gethsiyal Augasta M
4. Vaishak Belle
5. Nadia Burkart
6. Marco F. Huber
7. Ioannis Papantonis

Citations in the corpus

• Rule Extraction Algorithm for Deep Neural Networks: A Review (2016, background): “Moreover, extracted rules can be used for hazard mitigation, traceability, system stability and fault tolerance, operational verification and validation, and more [13].”
• Reverse Engineering the Neural Networks for Rule Extraction in Classification Problems (2012, background): “Rule extraction changes a black box system into a white box system by translating the internal knowledge of a neural network into a set of symbolic rules [11].”