基于PSO-BP的嵌入式大气数据系统算法研究
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南京航空航天大学

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TN06;V241

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国家自然科学基金项目(面上项目,重点项目,重大项目)


Research on flush air data sensing system algorithms based on PSO-BP
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    摘要:

    神经网络具有强大的函数拟合能力,可以被应用于嵌入式大气数据系统中进行大气数据估计。针对传统BP神经网络存在初始权值和阈值随机选取、训练过程局部最优、对训练数据需求量大等问题,提出了一种基于改进粒子群算法优化的神经网络,以提升FADS系统的预测精度。通过计算流体力学模拟,分别使用飞机在常规飞行和大迎角飞行状态下的压力数据来验证该算法的性能。结果表明,在训练数据有限的条件下,基于改进粒子群算法优化的神经网络在这两种飞行状态下均显著提高了大气数据的预测精度。常规飞行状态,静压、马赫数、迎角和侧滑角的预测误差分别降低了54.88%、60.46%、53.76%和62.12%;大迎角飞行状态下,预测误差分别降低了71.96%、47.52%、66.96%和53.41%。此外,在相同数据样本下,基于改进粒子群算法优化的神经网络在多次训练中误差波动更小,显示出更高的稳定性和可信度。

    Abstract:

    Neural networks possess strong function approximation capabilities and can be applied to Flush Air Data Sensing systems for air data estimation. Addressing the issues of random initial weights and thresholds, local optima during training, and high training data requirements inherent in traditional BP neural networks, an improved Particle Swarm Optimization algorithm-based neural network is proposed to enhance the prediction accuracy of the FADS system. The performance of this algorithm is validated through Computational Fluid Dynamics simulations, using pressure data from aircraft in both conventional flight and high angle of attack flight states. The results indicate that, under conditions of limited training data, the PSO-optimized neural network significantly improves air data prediction accuracy in both flight states. In conventional flight, the prediction errors for static pressure, Mach number, angle of attack, and sideslip angle are reduced by 54.88%, 60.46%, 53.76%, and 62.12%, respectively; while in the high angle of attack flight state, the prediction errors are reduced by 71.96%, 47.52%, 66.96%, and 53.41%, respectively. Furthermore, with the same data samples, the PSO-optimized neural network exhibits smaller error fluctuations across multiple training runs, demonstrating higher stability and reliability.

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  • 收稿日期:2024-08-30
  • 最后修改日期:2024-11-06
  • 录用日期:2024-11-07
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