Chaotic Signal De-Noising Based on Threshold Selection Rules with SNR Evaluations of Wavelet
Hai Xin Sun, Gao Huiwang, Ruan Xuejing · International Journal of Signal Processing Image Processing and Pattern Recognition · 2016
In nature, the observed Chaos phenomenas were often mixed with noise, the existence of noise made the prediction of chaotic time series generate large errors.Chaotic time series had the characteristic of broadband, which liked noise.So there were some limitations with the traditional method of de-noising.But the wavelet threshold de-noising method had the characteristic of the multi-resolution analysis, and its computational quantity was smaller and the noise filtering effect was better.On the other hand, for different types of signals, with different wavelet base functions and threshold rules, it might have a different effect on the de-noising effect.In order to search for the optimal selection of those parameters, firstly this paper constructed a simulated Lorenz noisy signal, and used this signal to do the de-noising experiment, used the SNR and RMSE as the evaluating indicator, and finally obtained the matching combination of those parameters.At the end of this paper, the de-noising simulation was carried out using China's Shijiao station runoff time series data from 1960 to 1970 in China, and the final results showed the effectiveness of the proposed method in this paper.