An the investigation of an aircraft crash the flight recorder data, the voice recorder tracks, and the inspection of the aircraft wreckage are key elements. At times these elements are not sufficient to allow the investigators to reach a conclusion about a cause or a set of causes for the failure(s) leading to the crash. Federal rules do not mandate for a "black box" to record deflections of the aircraft control surfaces. Nevertheless, it is clear that these dynamic time histories can provide critical information leading to the discovery of the cause(s) of the crash. In this paper a neural network approach is proposed for the reconstruction of the time histories of the control surfaces deflections. The neural inputs are the discretized "black box" data; the neural outputs are the estimates of the control surface deflections which minimize weighted quadratic differences between the time histories data from a parallel simulation code and the actual "black box" data. The approach is iterative in nature; starting from an initial time instant, the signal reconstruction scheme will proceed to the next time instant only when the estimates of the control surfaces deflections at the current instant minimize the weighted quadratic differences relative to the next time instant. The results relative to the reconstruction of the control surfaces deflections for a single type of failure under different conditions are presented and discussed.
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