| 汽车悬架系统的RBF网络模型辨识 |
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| 引用本文： | 曲春英.汽车悬架系统的RBF网络模型辨识[J].农业装备与车辆工程,2007,4(5):21-24. |
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| 作者姓名： | 曲春英 |
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| 作者单位： | 海南师范大学物理系,海南,海口,571158 |
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| 基金项目： | 海南师范大学青年教师资助项目 |
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| 摘 要： | 利用小波良好的去噪性能，选择了合适的参数对越野吉普车BJ2020S实验测量信号进行小波去噪。运用BA3F网络具有逼近任何非线性函数且具有自学习和自适应的能力，建立了汽车悬架系统的非线性模型。通过与BP网络的比较．辨识结果表明：BA3F神经网络辨识精度高，响应速度快，小波和BA3F神经网络相结合是一种有效的系统辨识方法。
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| 关 键 词： | 系统辨识 RBF网络 汽车悬架 小波去噪 |
| 文章编号： | 1673-3142(2007)05-0021-03 |
| 修稿时间： | 2007年1月14日 |
Identification of Vehicle Suspension System Using RBF Neural Network |
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| QU Chun-ying.Identification of Vehicle Suspension System Using RBF Neural Network[J].Agricultural Equipment & Vehicle Engineering,2007,4(5):21-24. |
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| Authors: | QU Chun-ying |
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| Abstract: | It is an effective method to reduce the noise in signal by wavelet, and proper parameters are selected to reduce the noise in test signal of BJ2020S road-off jeep in this paper. A nonlinear model of vehicle suspension system is developed using RBF neural networks identification technology. The result indicates that the RBF neural network is an effective system identification method, which has merit of high precision, fast speed and good stabilization. |
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| Keywords: | system identification RBF network vehicle suspension wavelet de-noising |
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