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

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Affiliation of Author(s):能源与动力学院

Journal:Tuijin Jishu

Abstract: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 No.:1001-4055

Translation or Not:no

Date of Publication:2017-06-01

Co-author:Li, Yong-Jin,Jia, Shuang-Long,Zhang Tianhong

Correspondence Author:zhb