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基于过程神经网络的液体火箭发动机状态预测

聂侥 程玉强 吴建军

聂侥, 程玉强, 吴建军等 . 基于过程神经网络的液体火箭发动机状态预测[J]. 北京航空航天大学学报, 2016, 42(8): 1675-1681. doi: 10.13700/j.bh.1001-5965.2105.0521
引用本文: 聂侥, 程玉强, 吴建军等 . 基于过程神经网络的液体火箭发动机状态预测[J]. 北京航空航天大学学报, 2016, 42(8): 1675-1681. doi: 10.13700/j.bh.1001-5965.2105.0521
NIE Yao, CHENG Yuqiang, WU Jianjunet al. Condition prediction of liquid propellant rocket engine based on process neural networks[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(8): 1675-1681. doi: 10.13700/j.bh.1001-5965.2105.0521(in Chinese)
Citation: NIE Yao, CHENG Yuqiang, WU Jianjunet al. Condition prediction of liquid propellant rocket engine based on process neural networks[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(8): 1675-1681. doi: 10.13700/j.bh.1001-5965.2105.0521(in Chinese)

基于过程神经网络的液体火箭发动机状态预测

doi: 10.13700/j.bh.1001-5965.2105.0521
基金项目: 国家自然科学基金(51206181,51506219)
详细信息
    作者简介:

    聂侥,男,博士研究生。主要研究方向:火箭发动机健康监控。E-mail:nieyao121@163.com;程玉强,男,博士,副研究员。主要研究方向:火箭发动机健康监控、液体火箭发动机减损控制。Tel.:0731-84575198。E-mail:393239162@qq.com;吴建军,男,博士,教授,博士生导师。主要研究方向:火箭及其组合推进技术、火箭发动机健康监控。Tel.:0731-84575198。E-mail:jjwu@nudt.edu.cn

    通讯作者:

    吴建军,Tel.:0731-84575198,E-mail:jjwu@nudt.edu.cn

  • 中图分类号: V434.1;TP277

Condition prediction of liquid propellant rocket engine based on process neural networks

  • 摘要: 提出一种基于极限学习算法的离散过程神经网络模型,用于解决液体火箭发动机状态预测这一难题。首先,在历史数据的基础上建立离散过程神经网络(DPNN)预测模型;然后,根据在线更新的数据样本,采用递推极限学习(EL)算法对双并联前馈离散过程神经网络(DPFDPNN)隐层到输出层的权值进行更新,并应用权值更新后的过程神经网络对发动机状态进行预测;最后,以液体火箭发动机状态预测中氢涡轮泵扬程预测为例,分别采用有权值更新和无权值更新两种预测模型进行了试验。结果表明,通过更新过程神经网络权值可以使模型具有更高的预测精度和更好的适应能力,该方法能够为液体火箭发动机状态预测提供一种有效的解决途径。

     

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出版历程
  • 收稿日期:  2015-08-10
  • 网络出版日期:  2016-08-20

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