Page 137 - 《爆炸与冲击》2026年第5期

第 46 卷 第 5 期 爆 炸 与 冲 击 Vol. 46, No. 5
2026 年 5 月 EXPLOSION AND SHOCK WAVES May, 2026

DOI：10.11883/bzycj-2025-0152

基于变分模态分解处理的冲击波压力

长短期记忆网络系统建模 *

罗瑶嘉，张志杰

（中北大学仪器科学与动态测试教育部重点实验室，山西 太原 030051）

摘要： 冲击波压力传感器采集系统兼具高低频动态特性，而传统的基于传递函数的建模方法难以实现整体精准
建模，这一问题限制了系统补偿精度的提升。本文提出一种基于麻雀优化算法、变分模态分解和长短期记忆网络的动
态特性融合建模方法，旨在解决整体建模难题并提高系统动态特性建模精度。该方法通过优化算法搜索变分模态分
解的模态数和惩罚因子，自适应分解响应信号为多个模态分量并识别成分，实现高频与低频分量的有效分离；对低频
分量进行动态特性补偿后，将其作为压力信号和原响应信号构建模型输入输出数据集，通过网络完成传感器系统动态
特性建模。仿真与实爆试验结果表明，相较于传统的反滤波补偿方法，本方法补偿后信号与典型压力曲线的平均绝对
百分比误差降低 75%，振荡残余减小 38%，满足作为输入压力信号的精度要求；与单一神经网络建模相比，该融合建模
方法的误差降至 13%，为解决传感器宽频带动态建模难题提供了一条有效途径。
关键词： 冲击波压力；动态补偿；变分模态分解；长短期记忆
中图分类号： O384 国标学科代码： 13035 文献标志码： A

Shock wave pressure modeling using long short-term memory network
based on variational mode decomposition processing

LUO Yaojia, ZHANG Zhijie
（Key Laboratory of Instrumentation Science and Dynamic Measurement, Ministry of Education,
North University of China, Taiyuan 030051, Shanxi, China）

Abstract: hock wave pressure sensor acquisition systems exhibit both high- and low-frequency dynamic characteristics;
however, traditional transfer-function-based modeling and compensation methods cannot achieve accurate full-band
representation, thereby limiting further improvements in compensation accuracy and reconstructed signal fidelity under
complex dynamic conditions. To overcome this limitation, a fusion modeling method integrating the sparrow search algorithm

(SSA), variational mode decomposition (VMD), and a long short-term memory (LSTM) network was developed to enhance the
dynamic characteristic modeling accuracy of shock wave pressure acquisition systems. In this method, SSA was employed to
globally optimize the mode number and penalty factor of VMD using a comprehensive fitness function that combined sample
entropy and the Pearson correlation coefficient, thereby improving the adaptability of the decomposition to nonstationary
response signals contaminated by oscillations and noise. With the optimized parameters, VMD decomposed the sensor
response signal into multiple intrinsic modal components; the frequency-domain characteristics of each component were then
analyzed, and correlation coefficients together with jump durations were calculated and compared according to the spectral
distribution characteristics of shock waves to identify the signal types contained in each mode. Based on this identification,
high-frequency oscillatory modes and noise modes were discarded, enabling reconstruction of the effective shock wave signal.

* 收稿日期： 2025-05-26；修回日期： 2026-01-07
第一作者： 罗瑶嘉（2001－ ），女，硕士研究生，13554081002@163.com
通信作者： 张志杰（1965－ ），男，博士，教授，博士生导师，zhangzhijie@nuc.edu.cn

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