Volume 26 Issue 7
Jul.  2011
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GUO Rong-hua, WU Yu-sheng, CHEN Qing-rong. Application of neural networks in the test for electronic-controlled gasoline engine of unmanned aerial vehicle[J]. Journal of Aerospace Power, 2011, 26(7): 1672-1680.
Citation: GUO Rong-hua, WU Yu-sheng, CHEN Qing-rong. Application of neural networks in the test for electronic-controlled gasoline engine of unmanned aerial vehicle[J]. Journal of Aerospace Power, 2011, 26(7): 1672-1680.

Application of neural networks in the test for electronic-controlled gasoline engine of unmanned aerial vehicle

  • Received Date: 2010-06-23
  • Rev Recd Date: 2011-03-04
  • Publish Date: 2011-07-28
  • The characters and difficulties in the test for electronic-controlled gasoline engine of unmanned aerial vehicle (UAV) were analyzed in detail.For mending the traditional liner interpolation methods,which are not completely in conformity with nonlinear characteristics of electronic-controlled gasoline engine,a solution was applied in the predication for the manifold air pressure (MAP) of high-altitude calibration of electronic-controlled gasoline engine,by adopting back propagation (BP) neural networks.To avoid the limitation of neural networks in application at present stage,a grouping method of raw-data was put forward to control the feedback of the training error in real-time;this method provided a good solution for the problem that the neural network training result easily leads to the situation of "local optimum" and "over-fitting".The prediction results based on BP neural networks have been studied thoroughly;and the results of the training error and the prediction error reveal that the method is feasible and the result is effective.

     

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