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Randomized approach to verification of neural networks

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1 Author(s)
Zakrzewski, R.R. ; Fuel & Utility Syst., Goodrich Corp., Vergennes, VT, USA

Rigorous verification of neural nets is necessary in safety-critical applications such as commercial aviation. This paper investigates feasibility of a randomized approach to the problem. The previously developed deterministic verification method suffers from exponential growth of computational complexity as a function of problem dimensionality, which limits its applicability to low dimensional cases. In contrast, complexity of the randomized method is independent from the problem dimension. Verification of a neural net is formulated as Monte Carlo estimation of probability of failure. The required number of random samples is analyzed. Instead of the general Chernov-based bound, a significantly improved condition is found by exploiting the special case when the number of observed failures is zero. It is shown that with the currently available computers the method is a viable alternative to the deterministic technique. Issues regarding possible acceptance of statistical verification by certification authorities are also, briefly discussed.

Published in:

Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on  (Volume:4 )

Date of Conference:

25-29 July 2004