Key result
Stacked Gaussian Process Network outperforms seven competitive peers in approximating flight height differences.
Why the study?
The relationship between flight performance and multi-physiological parameters under different flight operating patterns is unknown.
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A novel Stacked Gaussian Process Network (SGPN) model outperforms competing models in inferring flight performance from pilots' physiological parameters.
The relationship between flight performance and multi-physiological parameters under different flight operating patterns is unknown. This work proposes a Stacked Gaussian Process Network (SGPN) to reveal it. SGPN is a multi-layer network model formed by recursion from a regular Gaussian process and random disturbance. This work constructs an auxiliary variable strategy with the induced points to improve its learning efficiency, thus leading to a sparse SGPN model. In it, a Gaussian process acts as an activation function of each node, but the entire model is no longer a Gaussian process and thus very challenging to solve it. This work presents its solution via variational approximate inference. Experimental results of pilot flight performance evaluation show that the proposed model has stronger learning and generalization ability than its seven competitive peers. It is able to approximate non-linear coupling relationship between multi-physiological parameters and flight height differences.
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Wu et al. (2021) studied Pilot flight performance. Stacked Gaussian Process Network (SGPN) vs. Seven competitive peers was evaluated on Flight height differences. The proposed Stacked Gaussian Process Network (SGPN) demonstrated stronger learning and generalization ability than seven competitive peers in approximating flight height differences.
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