The research of an effective denoising algorithm for the actual obtained signals with chaotic characteristics is of great interest to all fields of the subject. To show the true chaotic state and analyze the dynamic characteristics of the chaotic system more accurately, an improved denoising algorithm using wavelet packet is proposed in this paper. Wavelet packet decomposition has an optimal sub-band tree structure, which can be used for local analysis of chaotic signals. Based on the correlation function value differences of wavelet packet coefficients, the algorithm determines the optimal decomposition layer, while the optimal wavelet packet basis is obtained with logarithmic energy entropy as the cost function. Furthermore, on the one hand, it makes efforts to divide wavelet packet coefficients into approximate parts, fuzzy parts and detail parts. On the other, it carries out singular spectrum analysis, the fuzzy threshold and the correlation analysis for the select of these three different types of coefficients in order to retain the dynamic performance of chaotic signals in the greatest extent. To verify the effectiveness of the algorithm, the Lorenz chaotic model is employed to analyzed. Simulation results verify the practicability of the improved denoising algorithm, which can also be well applied to various chaotic signals denoising with different noise levels.

对于实际获得的具有混沌特性的信号的有效去噪算法的研究对于该主题的所有领域都具有极大的兴趣。为了显示真实的混沌状态并更准确地分析混沌系统的动态特性，提出了一种改进的基于小波包的去噪算法。小波包分解具有最佳的子带树结构，可用于局部分析混沌信号。基于小波包系数的相关函数值差，确定最优分解层，同时以对数能量熵为代价函数获得最优小波包基础。此外，一方面，它努力将小波包系数划分为近似部分，模糊零件和细节零件。另一方面，针对这三种不同类型的系数的选择，它进行了奇异频谱分析，模糊阈值和相关性分析，以最大程度地保持混沌信号的动态性能。为了验证该算法的有效性，采用了Lorenz混沌模型进行了分析。仿真结果验证了该改进去噪算法的实用性，也可以很好地应用于各种噪声水平不同的混沌信号去噪。为了验证该算法的有效性，采用了Lorenz混沌模型进行了分析。仿真结果验证了该改进去噪算法的实用性，也可以很好地应用于各种噪声水平不同的混沌信号去噪。为了验证该算法的有效性，采用了Lorenz混沌模型进行了分析。仿真结果验证了该改进去噪算法的实用性，也可以很好地应用于各种噪声水平不同的混沌信号去噪。