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在系统测试过程中,由于各种干扰的存在,使得测试系统采集到的数据偏离其真实值。而干扰因素不仅包括一般意义下的测量误差,还包括测量理念的差异。混合数据信号造成相邻测量结果间的混叠,使得离散数据绘成的振动曲线上呈许多毛刺,很不光滑,为了消弱干扰信号的影响,提高振动曲线光滑度,常常需要对采样数据进行平滑处理。本文针对混合数据信号的特征,提出一种新的模糊平滑法,沿用加权平均的思想,对信号相邻的点之间进行加权运算。但是权因子的确立是通过建立信号尖端点的隶属度函数,以此评价信号在每一点的尖端程度,更准确实际地反映信号混合的程度。另外,基于模糊变量的期望值算子做出新的评价标准用来评价信号的平均尖端度。
During the system test, due to various interferences, the data collected by the test system deviates from its true value. The interference factors include not only the measurement error in the general sense, but also the difference between the measurement concepts. Mixed data signals caused by the aliasing between adjacent measurement results, the discrete data is drawn on the vibration curve showed a lot of glitches, very smooth, in order to weaken the interference signal to improve the smoothness of the vibration curve, often need to sample data Smoothing. Aiming at the characteristics of mixed data signals, a new fuzzy smoothing method is proposed in this paper, which adopts the idea of weighted average to weight the adjacent points of signals. However, the weighting factor is established by establishing the membership function of the signal tip, in order to evaluate the sharpness of the signal at each point and reflect the degree of signal mixing more accurately and practically. In addition, the expectation operator based on fuzzy variables makes a new evaluation criterion to evaluate the average signal tip.