非线性时间序列的小波分频预测

基于噪声的小波变换特点，结合小波包分解和模极大重构来抽取含噪信号的主分量，提出了一种基于最佳尺度分解和Volterra自适应滤波的分频预测算法，使用较少的模型训练样本，同时具有强的抗噪能力.该算法克服了传统小波分解尺度选取的盲目性及单纯Volterra预测器抗噪性能的不足，数值仿真表明，针对含强噪声的非线性信号可进行有效预测. 关键词： 小波分解 Volterra自适应滤波器 分频预测

A novel subband forecast method for nonlinear time series using wavelet transform

In this paper, a new method is proposed to implement subband forecast within the nonlinear noisy time series based on abstracting and reconstruction of the sign al's main components and adaptive Volterra filter theory.By considering noise's wavelet transform characteristic,the main component of noise signal is abstracte d by using the wavelet package decomposition in an appropriate scale and the ma ximum module reconstruction algorithm,then the forecast components are brought f rom adaptive Volterra forecast filter to reconstruction the final signal.This m ethod improves the traditional blindness in selecting scale in wavelet decomposi ng denoise,avoids the shortage of antinoise capability of Volterra series model used singly.The simulated results show that it is a practicable and effective me thod for nonlinear noise signal.