Small sample data-based meta-learning algorithm for equipment quality prediction

To solve the difficulty of munition equipment quality prediction under small sample data, a munition equipment quality prediction model based on meta-learning algorithm is proposed in this paper. Firstly, the pre-processed ammunition equipment quality data is divided into multiple batches of task sequences to meet the input requirements of the small sample data meta-learning model. Secondly, the munition equipment quality meta-learning model is trained by a series of learning tasks to acquire the knowledge for predicting munition equipment quality, and when dealing with the tasks that have never been encountered before, the knowledge learned can help improve adaptability and generalization in learning these new tasks. Finally, the quality prediction validity of munition equipment quality meta-learning model is verified by open data set and munition equipment quality data set.