General regression neural network (GRNN) has a good nonlinear mapping ability.So exhaust gas temperature (EGT) is predicted by GRNN.But

its accuracy of prediction is affected by the width coefficient of GRNN.To address the problem

the GRNN optimized by the improved fruit fly optimization algorithm (IFOA-GRNN) was proposed. And it was used to predict EGT.Taking the engine as an example

some parameters were taken as input variables and EGT taken as output variable of prediction models.The forecast results of IFOA-GRNN

FOA-GRNN(fruit fly optimization algorithm to optimize GRNN)

GRNN

auto-regressive and optimized support vector regression were compared under the same training samples and testing samples.The experiment results showed that the convergence accuracy of IFOA-GRNN was higher than FOA-GRNN. Average relative error of IFOA-GRNN for EGT prediction was 2.47%

and the goodness of fit was 0.8506

the prediction effect of IFOA-GRNN was better than other comparison algorithms.And it was more accurate than other methods in the prediction of aero-engine exhaust gas temperature under noisy and no-noise conditions.