思维进化算法优化小波包去噪仿真分析

Mind Evolutionary Algorithm Optimization and Wavelet Packet Denoising Simulation Analysis

小波包变换去噪的关键在于小波基函数、阈值、分解层数的选取,为了有效去除噪声,提高信噪比,提出一种思维进化算法优化的改进小波包去噪方法。传统的小波包阈值去噪产生了显著的效果,然而对于噪声分布不均的信号去噪存在一定的局限性。采用思维进化算法(MEA)寻找最优小波基函数和分解层数,再分段优化小波包阈值的去噪方法应用于信号降噪仿真。对比了传统的Sqtwolog、Rigrsure、Heursure;Minimaxi阈值规则的去噪效果,思维进化算法优化小波包去噪方法去噪效果更佳。

The key of wavelet packet transform denoising is the selection of wavelet basis function,threshold and decomposition layer.In order to effectively remove noise and improve signal-to-noise ratio,an improved wavelet packet denoising method based on Mind Evolution Algorithm is proposed.The traditional wavelet packet threshold denoising has a significant effect,but there are certain limitations on signal denoising with uneven noise distribution.According to the method,we used the Mind Evolution Algorithm(MEA)to find the optimal wavelet basis function and the decomposition layer number,and then used the improved threshold denoising method to apply to the signal noise reduction simulation test.After experimental analysis,the denoising effects of the traditional Sqtwolog,Rigrsure,Heursure and Minimaxi threshold rules were compared.The experimental results show that the wavelet packet denoising method optimized by the Mind Evolution Algorithm has better denoising effect.