| 基于差分进化算法优化的RBF神经网络的发动机故障诊断研究 |
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| 引用本文： | 周亨,彭涛,邓维敏. 基于差分进化算法优化的RBF神经网络的发动机故障诊断研究[J]. 农业装备与车辆工程, 2014, 0(2): 22-26 |
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| 作者姓名： | 周亨 彭涛 邓维敏 |
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| 作者单位： | 重庆市重庆交通大学, |
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| 摘 要： | 在RBF神经网络中采用差分进化算法来优化RBF神经网络的模型结构,并对其重要参数进行全局寻优。实例仿真结果表明,经过差分算法优化的RBF神经网络不仅相对BP网络学习收敛速度更快,而且提高了发动机故障识别的精确度,从而验证了此种方法的正确性和有效性。
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| 关 键 词： | 发动机 差分进化算法 RBF神经网络 故障诊断 |
Research on Fault Diagnosis of Engine Based on RBF Neural of DE Algorithm |
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| Zhou Heng,Peng Tao,Deng Weimin. Research on Fault Diagnosis of Engine Based on RBF Neural of DE Algorithm[J]. Agricultural Equipment & Vehicle Engineering, 2014, 0(2): 22-26 |
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| Authors: | Zhou Heng Peng Tao Deng Weimin |
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| Affiliation: | (Chongqing Jiaotong University, Chongqing 400074, China) |
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| Abstract: | A learning algorithm of differential evolution （DE） method is used to optimize the model structure of RBF neural network, and also to make a global optimization for its parameter. The simulation results show that the DE method not only has better learning than BP neural network, but also improves the accuracy of fault diagnosis, thus verifying the correctness and va- lidity of this method. |
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| Keywords: | engine differential evolution （DE） algorithm RBF neural network fault diagnosis |
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