Introductionpilot training effectiveness in a flight simulator depends on the fidelity of the simulation. While parameter estimation techniques provide a high level of fidelity for most flight regimes, aerodynarmc modeling for highly non-linear regimes is di5cult. In this paper, a recurrent neural network is shown to be capable of providing an adequate simulation for aircraft dynamic response during spinning flight. A method is developed to train the network with flight test data measuTed during actual spins in a light single engine aircraft, and is shown to be f%ri rl y capable of simulafing responses to control inputs for other spins not used in the training An added advantage ofthis approach is its simplicity and ease of implementation. d a k P Q R T V X
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