Test of nonlinearity of time series is very important for nonlinear time series analysis and study of chaotic dynamics.
时间序列的非线性检测对于非线性时间序列分析、混沌特性研究有着重要意义。
Nonlinear time series analysis is applied in analyzing heart sounds for acquiring hemodynamic character of partly or completely occluded coronary artery.
本文通过非线性时间序列分析的方法来分析心音时间序列，获得有关冠状动脉阻塞的血液动力学特征信息。
By using the method of nonlinear time series analysis and taking the Chinese language cognition as an example, the dynamic characteristics of language cognition system are discussed.
利用非线性时间序列分析方法，以汉语语言认知为例，探讨语言认知系统的动力学特性。
Next, the thesis analysis the characterization of Roundtrip time delay (RTT). The RTT time series collected from the Internet are studied statistically by using both linear and nonlinear methods.
其次，对网络时延(RTT)特性进行了分析，利用线性和非线性方法对从互联网上采集的RTT时间序列进行统计分析。
This paper focuses on the application of nonlinear dynamical methods in the analysis of time series.
本文主要研究非线性动力学方法在时间序列分析中的应用。
The methods, which combine time series analysis and neural networks, are especially studied and applied in the model-unknown nonlinear system.
特别针对模型未知的非线性系统，研究了时间序列分析和神经网络相结合的故障预报方法。
The nonlinear dynamic theory and the chaotic time series analysis method were adopted to examine the nonlinear characteristics of strong earthquake ground motions in this paper.
引入非线性动力学理论和混沌时间序列分析方法考察强震地面运动加速度时程的非线性特征。
There are many branches in the field of time series analysis using nonlinear tools, and nonlinear dynamical methods is one of them that springs up in these years.
可以用来研究时间序列的非线性工具有许多种，而其中非线性动力学方法则是近年来兴起的一个重要分支。
The Support Vector Regression(SVR)is used for the time series analysis and prediction to resolve the complex nonlinear system modeling problems.
用支持向量回归（SVR）的方法分析和预测时间序列，可解决复杂非线性系统的建模问题。
The lab has ten experiments, covering time and frequency domain analysis of linear system, root locus and series compensation of linear system, analysis of discrete and nonlinear system.
本虚拟实验室共设计了十个实验，内容涉及线性系统时域分析、线性系统的根轨迹、线性系统频域分析、线性系统串联校正、离散系统分析和非线性系统分析。
How to identify chaos is the foundation of analysis, prediction and control of nonlinear time series.
识别混沌是对非线性时间序列进行分析、预测、控制的基础。
Further, through the phase space reconstruction of relating time series and fractal analysis, discussion to nonlinear evolution of the system with varying heating power is delivered.
通过对系统相关时序的相空间重构与分维分析，探讨了系统随加热功率变化的非线性演化规律。
Mutual information (MI) analysis is a general method to detect linear and nonlinear statistical dependencies between time series.
交互信息是一种检测系统之间相依性的方法，它可以同时检测线性和非线性相关。
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。
With this method, the transient analysis of nonlinear time-varying RLCM network can be reduced to a series of the DC analyses of linear time-invariant resistive network.
采用这种方法可将非线性时变RLCM网络的瞬态分析简化为若干次线性时不变电阻性网络的直流分析。
With this method, the transient analysis of nonlinear time-varying RLCM network can be reduced to a series of the DC analyses of linear time-invariant resistive network.
采用这种方法可将非线性时变RLCM网络的瞬态分析简化为若干次线性时不变电阻性网络的直流分析。
应用推荐