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| 基于改进的灰色RBF模型的初始备件预测研究 | |
| 引用本文： | 黄勇,孙德翔,邢国平,刘玉伟.基于改进的灰色RBF模型的初始备件预测研究[J].飞机设计,2011,31(6):44-46,57. |
| 作者姓名： | 黄勇 孙德翔 邢国平 刘玉伟 |
| 作者单位： | 1. 空军航空大学研究生队,吉林长春,130022 2. 空军航空大学训练部,吉林长春,130022 3. 空军航空大学航空机械工程系,吉林长春,130022 |
| 摘 要： | 针对初始备件故障规律不稳定和历史消耗数据少的特点，将遗传算法、GM（1，1）模型和RBF神经网络有机结合．提出了改进的灰色RBF预测模型。该模型利用遗传算法对GM（1，1）模型参数进行动态寻优，并利用RBF网络对预测值进行残差修正。最后，通过采集新机部队某初始备件上半年的消耗数据，对该模型进行了实例仿真。结果表明：改进... |
| 关 键 词： | 初始备件 遗传算法 GM(1 1)模型 RBF神经网络 |
Research of Prediction on Initial Spare Parts Based on Optimized Grey RBF Model |
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| HUANG Yong,SUN De-xiang,XING Guo-ping,LIU Yu-wei.Research of Prediction on Initial Spare Parts Based on Optimized Grey RBF Model[J].Aircraft Design,2011,31(6):44-46,57. | |
| Authors: | HUANG Yong SUN De-xiang XING Guo-ping LIU Yu-wei |
| Institution: | 1. Brigade of Graduate, Aviation University of Air Force, Changchun 130022, China ) ( 2. Department of Training, Aviation University of Air Force, Changchun 130022, China ) ( 3. Department of Aviation Mechanical Engineering, Aviation University of Air Force, Changchun 130022, China ) |
| Abstract: | Aiming at initial spare parts＇ characteristics of unstable fault rule and little historical data, a prediction model combining genetic algorithm, GM（1,1） model and RBF neural network was proposed in this paper. The model optimized the parameter of GM（1,1） model through genetic algorithm and gave the predictive values an error adjustment through RBF network. Lastly, half-year dada of an initial spare parts in new-plane troops was collected for the simulation of this model. The results show that the proposed model has high predictive precision comparing with GM（1,1） model and optimized GM（1,1） model, which can be applied in the short-term prediction of initial spare parts. |
| Keywords: | initial spare parts genetic algorithm GM（1 1） model RBF neural network |
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