| 引用本文: | 张玘,谢秀芬,刘国福,等.弹性BP神经网络消除轮速传感器误差方法的研究.[J].国防科技大学学报,2008,30(3):131-135.[点击复制] |
| ZHANG Qi,XIE Xiufen,LIU Guofu,et al.Research on Attenuating the Wheel Speed Sensor Errors Based on Resilient BP Neural Network[J].Journal of National University of Defense Technology,2008,30(3):131-135[点击复制] |
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| 弹性BP神经网络消除轮速传感器误差方法的研究 |
| 张玘, 谢秀芬, 刘国福, 刘波 |
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(国防科技大学 机电工程与自动化学院，湖南 长沙 410073)
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| 摘要: |
| 汽车轮速是汽车运动状态参数的主要信息源，是控制系统的核心，其精度直接影响这些系统的性能。为了提高轮速的精度，降低传感器的研制成本，提出了一种基于弹性BP神经网络的误差分析方法消除轮速传感器误差。将改进的BP神经网络——弹性BP神经网络用于误差分析，并提出误差匹配的算法。理论和仿真结果表明，该方法使绝对误差达到2×10-4 rad,能够有效地消除传感器误差，提高轮速信号的精度。 |
| 关键词: 弹性BP神经网络 轮速传感器 误差 |
| DOI： |
| 投稿日期：2007-11-19 |
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| Research on Attenuating the Wheel Speed Sensor Errors Based on Resilient BP Neural Network |
| ZHANG Qi, XIE Xiufen, LIU Guofu, LIU Bo |
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(College of Mechatronics Engineering and Automation, National Univ. of Defense Technology, Changsha 410073, China)
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| Abstract: |
| Being the main source of vehicles' movement state parameters, wheel speed is central for control systems and its accuracy affects their performance. In order to attenuate the wheel speed sensor errors and reduce the research and manufacture cost, an error estimation method based on resilient back propagation (BP) is presented. The improved resilient BP neural network is applied to estimate the sensor errors. Matching algorithm is illustrated to realize the corresponding errors. Theoretical analysis and simulation results show that the proposed method can make the error less than 2×10-4 rad, so it can effectively attenuate the sensor errors and improve the accuracy of the wheel speed signal. |
| Keywords: resilient back propagation neural network wheel speed sensor error |
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