基于EEMD和BPNN的动态误差溯源研究

针对动态测试系统在测试过程中存在误差导致精度损失的问题,提出了一种基于集合经验模态分解和BP神经网络的动态误差溯源方法.该方法在全系统动态精度理论的基础上,首先通过EEMD对动态测试系统输出总误差进行分解,对分解得到的单项误差进行希尔伯特变换,分析误差信号的幅频特性,然后采用BP神经网络拟合溯源.通过仿真分析,结果表明该方法可以有效地追溯到动态测试系统中误差产生的模块,并且偏差精度达到10-2,比经验模态分解的方法溯源效果更好,避免了EMD存在的模态混叠等问题,具有可行性和应用性.

Research of dynamic error tracing based on EEMD and BPNN

Aiming at the problem of precision loss caused by errors in dynamic testing system, a dynamic error tracing method based on ensemble empirical mode decomposition and BP neural network was proposed. Based on the theory of the whole system dynamic precision, the method decomposed the total output error of the dynamic test system through the EEMD, and performed Hilbert transform on the single error obtained by the decomposition, analyzed the amplitude-frequency characteristics of the error signals, and then used BP neural network to fitting and tracing. The simulation results show that the method can trace back the error module in the dynamic test system effectively, and the accuracy of deviation is up to 10-2, compared with empirical mode decomposition, the method has better traceability effect and avoids mode aliasing problems existing in EMD, it is feasible and applicable.