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| Corrosion Prediction of Vehicle Equipment Cooling System Based on Grey Compensation BP Neural Network Combined Model |
| Received:September 12, 2018 Revised:November 25, 2018 |
| View Full Text View/Add Comment Download reader |
| DOI：10.7643/ issn.1672-9242.2018.11.023 |
| KeyWord:vehicle equipment cooling system MUGM (1,1,λ) genetic algorithm BP neural network |
| Author | Institution |
| XU An-tao |
a. Projection Equipment Support Department, Army Military Transportation University, Tianjin , China |
| LI Xi-dong |
b. Postgraduate Training Brigade, Fifth Team of Cadets, Army Military Transportation University, Tianjin , China |
| ZHOU Hui |
b. Postgraduate Training Brigade, Fifth Team of Cadets, Army Military Transportation University, Tianjin , China |
| QIAO Yuan-bo |
b. Postgraduate Training Brigade, Fifth Team of Cadets, Army Military Transportation University, Tianjin , China |
| WU Zheng-ri |
b. Postgraduate Training Brigade, Fifth Team of Cadets, Army Military Transportation University, Tianjin , China |
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| Abstract: |
| Objective To avoid damage to the vehicle equipment cooling system due to corrosion, which disenable the vehicle to maintain a good working condition and shorten the service life of the equipment, and establish an accurate and efficient prediction model to predict corrosion of the vehicle equipment cooling system. Methods Based on the traditional GM (1,1) model, combined with background value construction optimization and metabolism, a metabolic weighted unequal time interval model MUGM(1,1,λ) was established. In addition, the genetic algorithm was used to optimize the BP neural network model to modify the residual of MUGM(1,1,λ) model, and the grey compensation BP neural network optimization combination model was established. Results Based on the optimized combination model, the average error of corrosion prediction for cast iron materials for cooling system was 0.43%; the model accuracy was level one; and the prediction accuracy was high. Conclusion The gray compensation BP neural network optimization combination model is feasible for the prediction of metal corrosion in vehicle cooling equipment. |
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