Near-optimal flight load synthesis using neural nets

This paper describes the use of neural networks for near-optimal helicopter flight load synthesis (FLS), which is the process of estimating mechanical loads during helicopter flight, using cockpit measurements. First, modular neural networks are used to develop statistical signal models of the cockpit measurements as a function of the loads. Then Cramer-Rao maximum a-posteriori bounds on the mean-squared error are calculated. Then, multilayer perceptrons for FLS are designed which approximately attain the bounds. It is shown that all of the FLS networks have good generalization.

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