基于动态滑动模型的加速度传感器数据在线预测方法研究

加速度传感器输出值精确测量是相关数据预测的必要前提,为补偿制造工艺和测量环境影响带来的加速度传感器输出误差并准确预测加速度传感器输出数值,提出了基于自适应归一化奇异谱和神经网络的加速度传感器误差补偿及数值预测方法。首先分析加速度传感器输出误差产生的原因;然后根据奇异熵定阶去噪的方法提出了自适应奇异谱方法用于加速度传感器误差自适应补偿;最后选用基于滑动窗的径向基(radical basis function, RBF)神经网络作为加速度传感器输出数值预测方法,并用粒子群优化算法优化RBF神经网络的初始参数。实验结果表明,自适应奇异谱方法可以有效补偿加速度传感器输出误差,并可以选定不同的自适应参数以满足不同误差需求,并且粒子群算法优化的RBF神经网络可以有效预测加速度传感器输出数值。

Research on on-line prediction of acceleration sensor data based on dynamic sliding model

Accurate measurement of the acceleration sensor output value is a necessary prerequisite for the prediction of relevant data. In order to compensate the output error of accelerometer sensor caused by manufacturing process and measurement environmental impact and accurately predict the output value of accelerometer sensor, an acceleration sensor error compensation and numerical prediction method based on adaptive singular spectrum and neural network is proposed. Firstly, the cause of the output error of the acceleration sensor is analyzed. Secondly, an adaptive singular spectral method is proposed for the acceleration sensor error compensation according to the singular entropy order determination denoising method. Finally, the radial basis function (RBF) neural network is selected as the numerical prediction method for the acceleration sensor output data, and the particle swarm optimization algorithm is used to optimize the initial parameters of the RBF neural network. The experimental results show that the adaptive singular spectral method can effectively compensate the output error of the acceleration sensor, and different adaptive parameters can be selected to meet different error requirements, and the RBF neural network optimized by the particle swarm optimization algorithm can effectively predict the output value of the acceleration sensor.