General functions may be represented as Wavelet series. A kind of stabile, efficient and fast algorithm of Wavelet transform may be gotten through multi-resolution analysis.
一般函数都可以写成小波级数的形式，而由多分辨分析可得到小波的稳定有效快速变换算法。
As for the stable stochastic noise component linearly superposed on the median series, it is smoothed by means of the default soft threshold wavelet de-noising.
对线性叠加在中值系列中的平稳随机噪声，用默认软阈值小波消噪平滑中值序列中的随机噪声分量。
Time-frequency distribution series based on wavelet transform is discussed., and a new time-frequency tool: frequency-shear distribution is introduced into radar imaging fields.
讨论了基于小波变换的时频分布级数，把一种新的时频分析工具——频率-切变分布应用于时频成像。
Part two: This paper analyzed the degree of predicting precision affected by decomposing scale coefficient of wavelet cycle model and various kinds of reservoir inflow runoff series.
第二部分，本文分析了小波周期模型的分解尺度数和各种不同入库径流序列对模型预测精度的影响程度。
Combining wavelet analysis and neural network characteristics, the error back propagation wavelet neural network based structure and algorithm to ship roll time series prediction are given.
结合小波分析和神经网络的特点，建立了应用于船舶横摇运动时间序列预报的误差反传小波神经网络结构并给出了算法。
The maximum entropy deconvolution is performed under the premise that seismic wavelet is of minimum phase, so the forward prediction error is equivalent to reflection coefficient series.
最大熵反褶积是在地震子波为最小相位条件下进行的，其向前预测误差就等于反射系数序列。
A new method is proposed to predict the stock market based on wavelet packet transformation and chaos theory, which can not only describe a time series but also capture the features of the chaos.
基于小波包变换和混沌理论提出了一种股票市场建模及其预测的新方法，既能刻划时间序列的规律，又能捕捉混沌状态的特征。
Probing to characters of subsidence series, we study on application of polynomial regression, wavelet denoising and frequency analysis on subsidence series processing.
针对沉降数据序列的特征，研究了多项式回归方法、小波降噪方法、频谱分析法在沉降数据处理中的应用。
Finally, the forecasting results of chaotic models are reconstructed based on wavelet packet theory. By doing so, the forecasting of system feature reference data series can be made.
最后，基于小波包理论将混沌模型预测的结果予以小波包重构，实现对系统特征参数序列的预测。
Wavelet network based nonlinear time series prediction model is submitted, and nonlinear time series prediction and its application in fault prediction are discussed in this paper.
本文提出了基于小波网络的非线性时间序列预报模型，探讨了非线性时间序列预报在故障预报中的应用。
The results indicate that the wavelet transform is an effective tool for nonstationary stochastic series analysis, which has great potential in hydrology and water resources research.
研究结果表明，小波变换是分析非平稳随机时间序列的有效工具，在水文水资源领域应用潜力很大。
This paper describes an Inverse Wavelet Transformation (IWT) approach to simulate target wind series with intermittency for given spectral functions as the input of structural wind responses analysis.
研究生成既满足给定的工程谱特性，又具有间歇性结构的风速序列作为结构气动计算的输入。
Aiming at the issue about multi-step prediction of the traffic flow chaotic time series, a fast learning algorithm of wavelet neural network (WNN) based on chaotic mechanism is proposed.
针对交通流量混沌时间序列多步预测的问题，提出了一种基于混沌机理的小波神经网络(WNN)快速学习算法。
Prediction parameters preprocessing method is proposed which is based on time series analysis with wavelet transform method. And in this way, the prediction accuracy is increased.
提出了基于时间序列分析和小波变换方法的实测参数预处理方法，提高了预测精度。
Practical results show that, by using wavelet decomposition and reconstruction, this method can efficiently extract weak deformation characteristics from the observed data series having strong noises.
结果表明，借助于小波分解与重构，可有效地从强噪声干扰的观测数据序列中提取变形特征。
EMD method is a new method for analyzing nonlinear and non-stationary data, which has more advantage than wavelet analysis, and it can process short time series precisely.
EMD方法是对非平稳、非线性信号进行分析的一种新的时频分析方法。它比小波分析等方法具有更强的特性并能准确地处理非常短的数据序列。
To solve information redundancy problem of the existing time series phase space reconstruction method, this article introduced the wavelet transform to phase-space reconstruction.
为解决现有时间序列相空间重构方法中重构信息冗余的问题，本文将小波变换引入到相空间重构之中。
Finally, the forecasting results of chaotic models are reconstructed based on the wavelet packet theory and the forecasting result of the original time series can be obtained.
再将混沌模型预测的结果进行小波包重构，则得到原始时序的预测结果。
Based on the statistical self-similarity of hydrolgy time series, a new approach for estimating the fractal dimension by using successive wavelet transform coefficients is proposed.
根据小波多分辨率分析和水文序列的统计自相似性，提出了水文序列分形维数的小波估计方法，给出了其计算步骤。
The paper proposes application of Wavelet Neural Network in high-frequency time series calendar effects' study. At last, the paper proves that WNN is better than classical FFF regression.
提出了用小波神经网络（WNN）来定量研究高频金融时间序列“日历效应”，通过比较发现WNN是比弹性傅立叶形式（FFF）回归技术更具优势的方法。
The space of prediction and application of non-stationary time series were expanded through the combined model of wavelet analysis, gray and time series prediction methods.
将小波分析理论、灰色预测理论和时间序列预测法组合进行需水量的预测，为原始非平稳时间序列的预测应用拓展了空间。
The space of prediction and application of non-stationary time series were expanded through the combined model of wavelet analysis, gray and time series prediction methods.
将小波分析理论、灰色预测理论和时间序列预测法组合进行需水量的预测，为原始非平稳时间序列的预测应用拓展了空间。
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