航天器环境工程2015，Vol.32Issue(4)：357-360,4.DOI:10.3969/j.issn.1673-1379.2015.04.003
基于径向基函数神经网络的空间碎片撞击模式识别研究
The pattern recognition of space debris hypervelocity impact based on RBF neural network
摘要
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.关键词
RBF神经网络/空间碎片/超高速撞击/声发射/模式识别Key words
RBF neural network/space debris/hypervelocity impact/acoustic emission/pattern recognition分类
航空航天引用本文复制引用
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):357-360,4.基金项目
国家高技术研究发展计划(863计划)(项目编号:2013AA7060101) （863计划）
