On the earning data of the tests, temperature static model of gyro has been studied by the two ways of linear regression algorithm and of wavelet network identification.
在试验数据的基础上，分别使用线性回归算法和小波网络建立了陀螺仪的静态温度模型。
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。
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。
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