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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.
Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on (Volume:4 )
Date of Conference: 25-29 July 2004