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张海波(教授)

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  • 博士生导师  硕士生导师
  • 电子邮箱:
  • 所在单位:能源与动力学院
  • 职务:能源与动力学院控制工程系主任
  • 学历:南京航空航天大学
  • 办公地点:A10-538
  • 联系方式:zh_zhhb@126.com
  • 学位:工学博士学位
  • 所属院系:能源与动力学院
  • 学科:动力工程及工程热物理
    航空宇航科学与技术
    能源动力

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  • 论文成果

Research on Modeling Method of On-Board Engine Model Based on Sparse Auto-Encoder

发布时间:2020-01-13  点击次数:

  • 所属单位:能源与动力学院
  • 发表刊物:Tuijin Jishu
  • 摘要:In order to solve the problem of the low accuracy of the piecewise linear model in the development of the on-board engine model, based on the sparse auto-encoder, an adaptive on-board engine model with 10 inputs 11 outputs for the large envelope is proposed and designed, the model consists of steady and dynamic two parts. In the first place, a new similarity criterion is needed to compress the sample data, which can reduce the amount of data and the sampling time while retaining the main information. Steady on-board engine modeling work is completed by the BP algorithm with the simplified training data. In view of the huge amount of data needed in dynamic modeling, the BP algorithm is difficult to train. Dynamic on-board model is established based on the sparse auto-encoder. By the introduction of quasi steady state judgment logic, in the dynamic process the dynamic on-board model based on the sparse auto-encoder is used, while in the steady state process the steady on-board model based on the BP algorithm is used. Simulation results show that the on-board model obtained has excellent dynamic and steady state accuracy, good real-time performance and a small amount of storage. The dynamic accuracy is within 1%, the steady accuracy is within 0.6%, model computation time is within 1ms once with the storage capacity no more than 100kB. © 2017, Editorial Department of Journal of Propulsion Technology. All right reserved.
  • ISSN号:1001-4055
  • 是否译文:
  • 发表时间:2017-06-01
  • 合写作者:Li, Yong-Jin,Jia, Shuang-Long,张天宏
  • 第一作者:张海波
  • 通讯作者:张海波