| 嵌入式大气数据传感系统的改进算法 |
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| 引用本文： | 郑成军,陆宇平,何真.嵌入式大气数据传感系统的改进算法[J].中国航空学报,2006,19(4):334-339. |
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| 作者姓名： | 郑成军 陆宇平 何真 |
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| 作者单位： | College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China |
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| 基金项目： | 国家高技术研究发展计划(863计划) |
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| 摘 要： | 简要介绍了嵌入式大气数据传感系统及其空气动力学模型,提出了求解动、静压和修正参数的改进算法.改进算法首先应用Moore-Penrose广义逆矩阵对非线性方程组进行简化.然后采用改进迭代算法和BP神经网络求解动、静压和修正系数.改进算法在精度、可靠性和实时性上都能满足系统的要求.应用BP神经网络,达到系统要求的精度所需要的计算时间只相当于原有算法的5%,具有更大的实时性优势.
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| 关 键 词： | 嵌入式大气数据传感系统 广义逆矩阵 收敛性 BP神经网络 flush airdata sensing system general inverse convergence BP neural network |
| 文章编号： | 1000-9361（2006）04-0334-06 |
| 收稿时间： | 2005-12-07 |
| 修稿时间： | 2006-09-08 |
Improved Algorithms for Flush Airdata Sensing System |
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| ZHENG Cheng-jun,LU Yu-ping,HE Zhen.Improved Algorithms for Flush Airdata Sensing System[J].Chinese Journal of Aeronautics,2006,19(4):334-339. |
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| Authors: | ZHENG Cheng-jun LU Yu-ping HE Zhen |
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| Institution: | College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China |
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| Abstract: | The Flush Airdata Sensing (FADS) system and its pressure model are presented briefly. The improved algorithm for calculating the impact pressure, static pressure and modifying coefficient are studied. First, the non-linear equations are simplified using Moore-Penrose inverse. Then the impact pressure and static pressure are computed with the improved iteration and BP neural network. Both the two improved algorithms meet the requirements of the flush airdata sensing system on precision,reliability and speed. BP neural network has great advantages on real-time requirements, for it needs only 5% time to reach the required precision comparing to the original algorithm. |
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| Keywords: | flush airdata sensing system general inverse convergence BP neural network |
| 本文献已被 CNKI 维普 万方数据 ScienceDirect 等数据库收录！ |
