摘要
为消除干扰信号对车轮轮速信号的影响,保证ABS的有效控制,利用BP神经网络的非线性映射功能,通过对标准信号滤波样本(由小波变换获得)的学习,建立起从信号输入到信号输出的BP神经网络模型,该模型可以把滤波过程通过权值和阈值集中存储和记忆,从而通过网络的联想能力来实现滤波和信号处理。利用相关的仿真试验数据验证了所建模型的正确性。
To remove the effects of interferential signal on automobile wheel speed signal and guarantee the effectual control of ABS, this paper made use of the non-linear mapping capability of BP neural network, and established a BP neural network model from input signal to export signal by learning from normal signal filtering samples （obtained from wavelet transform）. This model could store and memorize filtering process through weight values and thresholds, and then signal filtering and processing could be realized through the envision of the neural network. Finally, the simulated results proved that the model is valid and suitable.
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2008年第1期1-3,共3页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家自然科学基金资助项目(项目编号:50375043)
关键词
汽车
防抱死制动系统
轮速信号
数学模型
BP神经网络
Automobiles, Anti-lock braking system, Wheel speed signal, Mathematical model, BP neural network
