【摘要】 在液压系统中，液压管路是实现压力传导功能的重要组成部分，其压力值的变化不容忽视。在环境误差等因素的影响下，液压管路的压力变化呈现非线性和不稳定性。为解决该问题，提出基于粒子群优化算法(PSO)改进的基于注意力机制(Attention)的长短期记忆神经网络(LSTM)的液压管路压力预测方案。用某飞机液压管路的压力检测值作为输入数据，实现液压管路某支路位置的压力预测，并完成预测结果的可视化。实验结果表明，该模型预测平均误差为1.78%,符合液压管路压力预测要求。更多还原

【Abstract】 Hydraulic pipeline, whose changes of pressure can’t be ignored, is the most necessary component to transfer the pressrue in hydraulic system. The change of pressure at hydraulic pipeline, which is influenced by factors such as environmental errors, is non-linear and instability. In order to solve the problems, a method that pressure prediction based on PSO-LSTM-Attention is proposed. The research uses datas which drive from the actual pressure detection of a plane. PSO-LSTM-Attention model is used by the research on the pressrue prediction at the certain branch of hydraulic pipeline and the prediction of the model are visualized. The experinmental results indicates that average error of the model prediction is 1.78%, which meets the requirement of pressure prediction at hydraulic pipeline.更多还原