非平稳过程 meaning in English
non-stationary process
Examples
- The main purpose is to discuss the stationarity of two classes of nonstationary processes after wavelet transformations
摘要主要讨论了两类非平稳过程小波?换后的平稳性。 - The results hold in stationary carr model , carr model with a unit root and other nonstationary processes which satisfy certain conditions and provide a theoretical basis for statistical inference
所得到的结论适用于已得到应用的平稳条件自回归极差模型，也适用于包含单位根的模型和满足条件的其他类型的非平稳过程，为模型的统计推断提供了理论基础。 - Applying ftp transformation may acquire adequate precision for dealing with steady variable signal in fault diagnosis of woodworking machine tool field . but ftp transformation ca n ' t completely reflect time change characteristic of signal in unsteady process
在木工机床的故障检测与诊断领域中对于平稳随机信号的处理，采用傅立叶变换可以具有足够的精度，但傅立叶变换难以全面反映非平稳过程的时变特性信号。 - In light of the limitation of fast fourier transform ( fft ) for the method of traditional spectrum analysis to analyze the unsteady signal , wavelet and wavelet analysis are made for the typical unsteady process signal of starting up and shut down with the good characteristic of simultaneous localization in both the time and the frequency domains based on the field test on the vibration of two - row placed units in lijiaxia hydropower station , in which the signal is decomposed into different frequency band , and then the weak signal is caught and the dominant frequency is picked up for the analysis of the vibration source
摘要基于李家峡水电站双排机组振动的现场试验研究，并且针对传统频谱分析方法傅立叶变换( fft )对于非平稳信号已力不从心这一缺陷，利用小波分析方法在时域和频域上同时具有良好的局部化性质，通过对开停机这一典型非平稳过程信号进行小波及小波包分析，将其分解到不同频带内，获取微弱信息和提取优势频率，并对其作振源分析，得出开停机初始时刻因水流不稳均出现强烈的振动现象，且低频段信号能量最大，开停机过程水流脉动压力和尾水涡带摆动是引起定子基础振动的主要原因。 - Meanwhile , adjusting and optimizing the structure of investment distribution on education should be given attention . the innovation of this article are rest with : 1 ) applying granger causal relations methods to test causal relationships between education investment and economy growth ; 2 ) using time series data to built econometrical model , emphasizing education investment ' s long term feature ; 3 ) projecting future developments by arima model
本文主要创新点在于: ( 1 )利用格兰杰因果关系检验确定教育投资与经济增长之间的因果关系; ( 2 )利用时间序列数据进行建模时,着重体现了教育投资的长效性这一重要的特殊性质; ( 3 )利用齐次非平稳过程的arima模型对我国未来教育投资进行了预测。