| 嵌入式飞行参数传感系统的神经网络算法 |
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| 引用本文： | 张斌,于盛林.嵌入式飞行参数传感系统的神经网络算法[J].航空学报,2006,27(2):294-298. |
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| 作者姓名： | 张斌 于盛林 |
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| 作者单位： | 南京航空航天大学 自动化学院, 江苏 南京 210016 |
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| 摘 要： | 对使用BP网络来代替嵌入式飞行数据传感(FADS)系统的空气动力学模型进行了研究。针对FADS系统的特点设计了一个具有双隐含层的BP网络模型并详细推导出了它的快速算法。文中不仅设计了数据集的产生方法,而且对数据集的划分进行了探讨。试验结果显示,动静压的平均绝对误差均在130Pa以内,可以满足FADS系统的设计要求。
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| 关 键 词： | 大气数据传感系统 神经网络 快速算法 训练集 |
| 文章编号： | 1000-6893(2006)02-0294-05 |
| 收稿时间： | 2004-09-20 |
| 修稿时间： | 2004年9月20日 |
Neural Network Algorithm for Flush Airdata Sensing System |
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| ZHANG Bin,YU Sheng-lin.Neural Network Algorithm for Flush Airdata Sensing System[J].Acta Aeronautica et Astronautica Sinica,2006,27(2):294-298. |
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| Authors: | ZHANG Bin YU Sheng-lin |
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| Institution: | College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China |
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| Abstract: | This paper use BP network to model the flush airdata sensing system instead of using aerodynamic model. In connection with the characteristics of FADS system, a neural network architecture with two hidden layers and its fast algorithm are designed. Not only the method of producing training patterns but also the technique of compiling training data are discussed in detail. As a result,the absolute mean errors of static pressure and impact pressure are all less than 130 Pa in both the trained area and the nearby area, which can meet the requirement for the design of the FADS system. |
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| Keywords: | airdata sensing system neural network fast algorithm training pattern |
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