测量噪声条件下基于扩展变量和最佳平方逼近的重构方法的研究
首发时间:2020-03-03
摘要:通过测量数据重构网络结构是复杂网络研究有意义的课题。基于扩展变量和最佳平方逼近的重构方法(reconstruction method based on variable expansion and least squares approximations, 简称VELSA)是由史润东等人最近提出的,该方法在非线性、强系统噪声和低采样频率条件下有很好的重构结果。由于测量环境和仪器精度的影响,采样数据会包含测量噪声。本文理论分析了测量噪声对该重构方法的影响,分析了平滑法对减小重构误差的作用。数值仿真进一步验证了理论分析的有效性。
关键词: 网络重构 测量噪声 扩展变量 最佳平方逼近 平滑法
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Reconstruction method based on variable expansion and least squares approximations with measurement noise
Abstract:To reconstruct network structures from measurable data has important significance in theory and practice. Shi et al propose a reconstruction method based on variable expansion and least squares approximations, named VELSA, and this method has a good reconstruction effect under the conditions of nonlinearity, strong system noise and low sampling. Due to the influence of measurement environment and instrument accuracy, sampling data include measurement noise. This paper theoretically studies the influence of measurement noise on VELSA, and analyzes the influence of smoothing method on reducing reconstruction ofreconstruction method based on variable expansion and least squares approximations with measurement noise error. The validity of the theoretical analysis is further verified by numerical simulations.
Keywords: Network reconstruction Measurement noise Variable expansion Least squares approximations Smoothing method
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测量噪声条件下基于扩展变量和最佳平方逼近的重构方法的研究
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