Probe measurement correction method based on genetic algorithm-optimized BP neural network

To address total pressure measurement errors caused by multiple factors in complex aero-engine flow fields, an error correction method based on a genetic algorithm-optimized backpropagation (GA-BP) neural network was proposed. Utilizing multi-sample datasets from numerical simulations, the GA-BP neural network was adopted: a 6-node hidden layer structure was determined, with logsig and purelin served as activation functions for the hidden and output layers respectively, and trainlm as the training function. An optimized combination of initial population size of 45, crossover probability of 0.8, and mutation distribution index of 5 reduced the root mean square error to 0.37591. Experimental results demonstrate that, compared with the standard BP model, the GA-BP model reduced root mean square error by 66.7% and the mean relative error by 72.4% on the test set, while achieving a coefficient of determination above 0.999; errors of 90% samples were below 2×10⁻³ and symmetrically distributed around zero. This proposed approach significantly enhances correction accuracy and generalization capability, providing a reference solution for aero-engine aerodynamic performance testing.