| 基于径向基函数神经网络的空间碎片撞击模式识别研究 |
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| 引用本文： | 杜刚,何朔,于海鹏.基于径向基函数神经网络的空间碎片撞击模式识别研究[J].航天器环境工程,2015,32(4):357-360. |
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| 作者姓名： | 杜刚 何朔 于海鹏 |
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| 作者单位： | 中国运载火箭技术研究院 研发中心, 北京 100076 |
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| 基金项目： | 国家高技术研究发展计划(863计划)(项目编号:2013AA7060101) |
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| 摘 要： | RBF神经网络是广泛应用的神经网络之一,可应用于航天器空间碎片撞击损伤模式的识别研究。采用AUTODYN仿真软件模拟产生高速撞击声发射信号,并随机提取其中部分信号,以信号幅值和撞击观测点等作为输入参数,以撞击速度作为输出参数,建立RBF神经网络,实现对空间碎片撞击速度的反演及穿透模式的识别。实际证明该神经网络能在一定程度上有效反演弹丸撞击速度。
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| 关 键 词： | RBF 神经网络 空间碎片 超高速撞击 声发射 模式识别 |
| 收稿时间： | 2015/3/17 0:00:00 |
| 修稿时间： | 2015/7/18 0:00:00 |
The pattern recognition of space debris hypervelocity impact based on RBF neural network |
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| Du Gang,He Shuo,Yu Haipeng.The pattern recognition of space debris hypervelocity impact based on RBF neural network[J].Spacecraft Environment Engineering,2015,32(4):357-360. |
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| Authors: | Du Gang He Shuo Yu Haipeng |
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| Institution: | Research and Development Center, China Academy of Launch Vehicle Technology, Beijing 100076, China |
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| Abstract: | The RBF neural network is one of the most widely used neural network. It is used in the pattern recognition of acoustic emission signals generated by the hypervelocity impact of space debris and the spacecraft. The high-speed impact acoustic emission signals are generated by using the numerical simulation software AUTODYN. Some signals are randomly extracted, the magnitude and impact observation points are used as the input parameters, the impact velocity as the output parameter. The RBF neural network is established to inverse the impact velocity of space debris and identify the impact defect types. Calculated results show the effectiveness of inversion to some extent. |
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| Keywords: | RBF neural network space debris hypervelocity impact acoustic emission pattern recognition |
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