This decomposition method is adaptive, and, therefore, highly efficient. Since the decomposition is based on the local characteristic time scale of the data, it is applicable to nonlinear and non-stationary processes.
由于分解是基于信号时域局部特征的,因此它特别适合用来分析非线性非平稳过程。
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Moreover we investigate the relations for Markov processes, martingales and stationary processes systematically.
此外，还系统地研究了马氏过程、鞅及平稳过程之间的关系。
Since the decomposition is based on the local characteristic time scale of the data, it is applicable to nonlinear and non-stationary processes.
由于分解是基于信号时域局部特征的，因此它特别适合用来分析非线性非平稳过程。
A new method, the sampling interval stat. analysis method, was set up to analyze the non-uniformly sampling signal of the wide-sense stationary random processes.
提出了一种通用性强、适用于广义平稳随机过程非均匀采样信号谱分析的新方法——采样间隔统计分析法。
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