Integrated Ferroelectrics ( IF 0.7 ) Pub Date : 2022-07-21 , DOI: 10.1080/10584587.2022.2072126 Wang Min, Lu Xikun, Zhou Yi-di
Abstract
Composite steel is the most commonly used material for artillery barrels. Ablation and wear of the steel material during artillery firing affect the life of the barrel. In this paper, we propose a new model that combines the advantages of convolutional neural network (CNN) and long and short-term memory network (LSTM) in feature extraction and memory prediction, respectively, using Nadam (Nesterov-accelerated Adaptive Moment Estimation) algorithm and Bayesian Optimization (BO) to optimize the model parameters. The improved accuracy compared to other prediction models demonstrates the feasibility and superiority of the model.
中文翻译:
基于贝叶斯优化CNN-LSTM的枪支寿命预测模型
摘要
复合钢是炮管最常用的材料。火炮射击过程中钢材的烧蚀和磨损会影响枪管的使用寿命。在本文中,我们提出了一种新模型,该模型结合了卷积神经网络 (CNN) 和长短期记忆网络 (LSTM) 在特征提取和记忆预测方面的优势,分别使用 Nadam (Nesterov-accelerated Adaptive Moment Estimation)算法和贝叶斯优化 (BO) 来优化模型参数。与其他预测模型相比,提高的准确性证明了该模型的可行性和优越性。