小波基在低压电力线信道有色背景噪声建模中的应用研究

小波马尔科夫链法可用于低压电力线信道中有色背景噪声的建模,但小波基函数的不同会对噪声的建模精度产生较大影响。基于常用的几种小波基函数对同一组有色背景噪声分别开展小波马尔科夫链仿真建模,并计算了建模前后的功率谱密度及其均方根误差。研究结果表明,Daubecies、Biorthogonal和Haar小波基函数中Daubecies小波基函数的建模精度较高,Haar小波基次之,Biorthogonal小波的建模效果较差。在这3种小波基函数中,Daubecies小波可选为有色背景噪声进行小波马尔科夫链建模的最佳小波基函数。

基于小波神经网络的低压电力线背景噪声建模

为增强电力线通信的抗噪能力,针对低压电力线信道噪声中的有色背景噪声和窄带噪声,提出一种基于小波神经网络的建模方法。首先,将背景噪声进行小波神经网络建模,对比所建模型输出噪声与测试噪声的时域波形及功率谱密度,计算两者功率谱密度的均方根误差;然后,对同一组背景噪声分别进行基于传统的小波马尔科夫链和小波神经网络的建模,并计算2种模型输出噪声与测试噪声的功率谱密度及其均方根误差。仿真结果表明,小波神经网络输出噪声与测试噪声的时域波形及功率谱密度均有着较一致的变化趋势,因此小波神经网络对低压电力线信道背景噪声的建模是有效的,对宽带噪声的建模效果更好。

Forecasting freight volume based on wavelet denoising and FG-Markov

To eliminate the grey bias and improve ant-jamming performance of the standard grey-Markov forecasting model,a forecasting model based on wavelet packet decomposition and fuzzy grey Markov(FG-Markov)is proposed considering the characteristics of randomness and nonlinearility of freight volume forecasting.Firstly,based on the data analysis ability of wavelet packet to non-stationary random signal,wavelet packet decomposition is used to improve the analysis ability of data signal by decomposing historical freight volume data into wavelet packet component.On this basis,FG-Markov chain is proposed to obtain the transfer probability matrix of wavelet packet coefficients by introducing fuzzy grey variables,and forecast the freight volume by reconstructing wavelet packet coefficients.Finally,an example of Lanzhou railroad hub is carried out in order to testify the validity and applicability of this forecasting model.Compared with neural network model and other forecasting models,the proposed forecasting model can improve the forecasting accuracy under the same conditions.The forecasting accuracy of wavelet packet decomposition and FG-Markov is not only greater than that of any other single forecasting models,but also superior to that of other traditional combinational forecasting models,which can meet the actual requirements of freight volume forecasting.