| 基于PSO⁃BP的石英挠性加速度计静态模型辨识 |
| Static model identification of quartz flexible accelerometer based on PSO⁃BP |
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| 中文摘要: |
| 为提升石英挠性加速度计静态模型的辨识精度，提出基于粒子群优化?反向传播（Particle Swarm Optimization?Back Propagation， PSO?BP）神经网络的静态模型辨识方法。通过PSO改进BP神经网络易陷入局部最优的缺陷，根据加速度计的输入和输出维度完成神经网络模型构建，利用PSO的全局搜索能力实现BP神经网络初始权重的优化。利用精密离心机开展实验，对该方法的应用效果进行验证，结果表明：与基于BP神经网络的石英挠性加速度计静态模型辨识方法相比，基于PSO?BP神经网络的石英挠性加速度计静态模型辨识方法具有更优的非线性系数解析能力，辨识均方误差MMSE（Mean Squared Error， MSE）降低2个数量级，为推动高精度机载惯性导航系统发展提供了技术支撑。 |
| 英文摘要: |
| To improve the identification accuracy of the static model of quartz flexible accelerometer, this study proposes a static model parameter identification method based on a Particle Swarm Optimization?Back Propagation (PSO?BP) neural network. This approach addresses the local optima susceptibility of Back Propagation (BP) neural networks through Particle Swarm Optimization (PSO) integration. The neural architecture is configured according to accelerometer input?output dimensions, where the PSO's global exploration capability optimizes the initial weight for the BP network. Precision centrifuge?based calibration experiments were conducted to validate the proposed method. Experimental results demonstrate that the PSO?BP neural network exhibits significantly enhanced capability in resolving nonlinear coefficients compared to the standard BP network, achieving a reduction of the mean squared error (MSE) by two orders of magnitude, which provides technical support for advancing the development of high?precision navigation technologies in airborne inertial navigation systems. |
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| 中文关键词: 石英挠性加速度计 静态模型辨识 粒子群优化⁃反向传播神经网络 均方误差 |
| 英文关键词:quartz flexure accelerometer static model identification PSO⁃BP neural network mean square error |
| 基金项目: |
| DOI：10.11823/j.issn.1674-5795.2025.03.07 |
| 引用本文：史有志, 冯仁剑, 李晓婷.基于PSO⁃BP的石英挠性加速度计静态模型辨识[J].计测技术,2025,45(3):78~84. |
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