Detecting Determinism in Human Posture Control Data

1998 ◽  
Vol 08 (01) ◽  
pp. 179-188 ◽  
Author(s):  
L. Y. Cao ◽  
B. G. Kim ◽  
J. Kurths ◽  
S. Kim
Keyword(s):  
Time Series ◽  
Statistical Methods ◽  
Nonstationary Time Series ◽  
Posture Control ◽  
Nonlinear Prediction ◽  
Control Data ◽  
Human Posture ◽  
Prediction Techniques

In this paper, determinism in human posture control data is investigated using the approach of nonlinear prediction. We first comment that one should be cautious of using some statistical methods to analyze nonstationary time series. Then we test the predictability of the human posture control data with different prediction techniques, and investigate how nonstationarity and noise affect the prediction results. Different time series are tested, including data from healthy and ill persons, and different predictabilities are found in different time series.

Segmenting mean-nonstationary time series via trending regressions

2012 ◽  
Vol 168 (2) ◽  
pp. 367-381 ◽  
Author(s):  
Alexander Aue ◽  
Lajos Horváth ◽  
Marie Hušková
Keyword(s):  
Time Series ◽  
Nonstationary Time Series

scholarly journals An Extended Time Series Algorithm for Modal Identification from Nonstationary Ambient Response Data Only

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Chang-Sheng Lin ◽  
Dar-Yun Chiang ◽  
Tse-Chuan Tseng
Keyword(s):  
Time Series ◽  
Arma Model ◽  
Identification Problem ◽  
Nonstationary Time Series ◽  
Modal Identification ◽  
Ambient Vibration ◽  
Time Varying ◽  
Varying Parameters ◽  
Response Data ◽  
Time Varying Parameters

Modal Identification is considered from response data of structural systems under nonstationary ambient vibration. The conventional autoregressive moving average (ARMA) algorithm is applicable to perform modal identification, however, only for stationary-process vibration. The ergodicity postulate which has been conventionally employed for stationary processes is no longer valid in the case of nonstationary analysis. The objective of this paper is therefore to develop modal-identification techniques based on the nonstationary time series for linear systems subjected to nonstationary ambient excitation. Nonstationary ARMA model with time-varying parameters is considered because of its capability of resolving general nonstationary problems. The parameters of moving averaging (MA) model in the nonstationary time-series algorithm are treated as functions of time and may be represented by a linear combination of base functions and therefore can be used to solve the identification problem of time-varying parameters. Numerical simulations confirm the validity of the proposed modal-identification method from nonstationary ambient response data.

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