Artificial Neural Network Identification of Burning Rate in Solid Fuel Ramjet

CHEN Xiao-qian; WANG Zhen-guo; TAN Jian-guo; YANG Tao; ZHANG Wei-hua

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Artificial Neural Network Identification of Burning Rate in Solid Fuel Ramjet

更新时间：2026-07-07

- Artificial Neural Network Identification of Burning Rate in Solid Fuel Ramjet
- Vol. 16, Issue 1, (2001)
- 作者机构：
  
  国防科技大学航天与材料工程学院,湖南,长沙,410073
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2001
- 稿件说明：
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CHEN Xiao-qian, WANG Zhen-guo, TAN Jian-guo, et al. Artificial Neural Network Identification of Burning Rate in Solid Fuel Ramjet[J]. 2001, 16(1).
DOI：

CHEN Xiao-qian, WANG Zhen-guo, TAN Jian-guo, et al. Artificial Neural Network Identification of Burning Rate in Solid Fuel Ramjet[J]. 2001, 16(1). DOI：

摘要

由于固体燃料冲压发动机(SFRJ)结构的特殊性

其燃速的预测比较困难。为探讨燃速对于不同飞行工况的依赖性

对模拟试验的结果采用BP人工神经网络进行了辨识

并将辨识结果与用最小二乘法辨识的结果进行比较。从辨识的结果来看

这种辨识方法具有精度高、处理实验数据迅速、能进行在线辨识等特点

能够较好地满足工程应用的要求。此外

在利用人工神经网络对类似的大样本系统进行辨识时

如果能采用一些数学上的处理技巧。

Abstract

In order to search the dependency of the burning rate on the flying conditions of SFRJ

we identify the results of the simulated experiments by means of BP artificial neural network

and compare the results with those obtained with the help of Least Square Method.It is shown that the presented method of identification features high precision

fast experimental data handling and on-line process identification

and is superior in engineering application.Furthermore

it will greatly enhance the speed of training if some proper mathematic techniques are adopted in the application of artificial neural network to identifying similar big sample system.

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Related Institution

Laboratory of Aerospace Servo Actuation and Transmission，Beijing Institute of Precision Mechatronics and Controls

常州工程职业技术学院机电与汽车工程学院

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University of the Chinese Academy of Sciences

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