Research on Solid Motor Performance Prediction Method Based on Deep Meta-Learning

Addressing the numerous limitations in solid motor performance experiments for aircraft power systems, such as high costs, specialized equipment requirements, specific experimental environments, and high risks, this paper proposes an artificial intelligence method based on deep meta-learning for engine performance prediction. This method employs model-agnostic meta-learning (MAML) and deep convolutional neural networks (DCNN) models. Firstly, thrust-time data is divided into different training tasks according to varying experimental conditions. The optimal model parameters for each task are obtained through inner-loop training, and the model initialization parameters are updated in the outer-loop. After iterative optimization of the inner-loop and the outer-loop, a model predicted the total impulse of solid engines with high accuracy is obtained, and finally it is tested for new tasks. The test results demonstrate that compared to DCNN without meta-learning, this method reduces the error on the test set significantly, with a maximum percentage error of 2.27%. This verifies the high-precision prediction ability of the meta-learning model for solid motor perfor-mance under small sample conditions.