一种参数优化的混沌信号自适应去噪算法

针对非线性自适应混沌信号去噪算法的参数优化问题, 考虑到最优滤波窗长受到不同因素的影响, 为提高该算法的自适应性, 提出一种滤波窗长自动最优化的判决准则. 依据混沌信号和噪声自相关函数的不同, 首先采用不同窗长对含噪混沌信号进行去噪, 然后计算每个窗长对应的残差自相关度(RAD), 最后通过对最小RAD所对应的窗长进行一定比例收缩实现窗长的最优化. 仿真结果表明, 该判决准则能够在不同条件下对滤波窗长进行有效的自动最优化, 提高了混沌信号去噪算法的自适应性.

A parameter optimization nonlinear adaptive denoising algorithm for chaotic signals

In the parameter optimization issue of nonlinear adaptive denoising algorithm for chaotic signals, the window length is affected by different factors. In this paper, a criterion is proposed for selecting the optimal window length. According to the difference in autocorrelation function between chaotic signal and noise, first, the different window sizes are used for denoising noisy chaotic signals. Then, the residual autocorrelation degree (RAD) of each window length is computed. Finally, the optimal window length is obtained by shrinking the window length corresponding to the minimum RAD. Simulation results show that this criterion can automatically optimize the window length efficiently under different conditions, which improves the adaptivity of the denoising algorithm of chaotic signals.