Rule Extraction from Dynamic Cell Structure Neural Networks Used in a Safety Critical Application

Marjorie Darrah, Brian Taylor, and Spiro Skias

This paper describes an algorithm to extract rules from a dynamic cell structure (DCS) neural network and the rationale for extracting these rules. The DCS is a form of self-organizing map (SOM) neural network that has been used in a real-time adaptive flight control application. The purpose for extracting rules in this instance is to determine whether such rules, along with other techniques, could be used in the verification and validation (V&V) of a neural network being used in a safety-critical role. This paper will explain the intelligent flight control application of the DCS, describe the method used for rule extraction, provide experimental results of the rule extraction techniques applied to several data sets, and examine the relevance of the rules to the V&V process.

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