Identification of Time-Variant Modal Parameters Using Time-Varying Autoregressive with Exogenous Input and Low-Order Polynomial Function

2009 ◽  
Vol 24 (7) ◽  
pp. 470-491 ◽  
Author(s):  
C. S. Huang ◽  
S. L. Hung ◽  
W. C. Su ◽  
C. L. Wu
2011 ◽  
Vol 308-310 ◽  
pp. 2560-2564 ◽  
Author(s):  
Xiang Rong Yuan

A moving fitting method for edge detection is proposed in this work. Polynomial function is used for the curve fitting of the column of pixels near the edge. Proposed method is compared with polynomial fitting method without sub-segment. The comparison shows that even with low order polynomial, the effects of moving fitting are significantly better than that with high order polynomial fitting without sub-segment.


Geophysics ◽  
1999 ◽  
Vol 64 (6) ◽  
pp. 1730-1734 ◽  
Author(s):  
Beatriz Martín‐Atienza ◽  
Juan García‐Abdeslem

New methods for 2-D modeling of gravity anomaly data are developed following an approach that uses both analytic and numerical methods of integration. The forward‐model solution developed here is suitable to calculate the gravity effect caused by a 2-D source body bounded either laterally or vertically by continuous functions. In our models, the density contrast is defined by a second‐order polynomial function of depth and distance along the profile. We present several examples to show that our models are capable of accommodating a broad variety of geologic structures.


2012 ◽  
Vol 479-481 ◽  
pp. 688-693
Author(s):  
Zi Ying Wu ◽  
Kun Shi

In this paper a new time varying multivariate Prony (TVM-Prony) method is put forward to identify modal parameters of time varying (TV) multiple-degree-of-freedom systems from measured vibration responses. The proposed method is based on the classical Prony method that is often used to identify modal parameters of linear time invariant systems. The main advantage of the propose approach is that it can analyze multi-dimensional nonstationary signals simultaneously. A modified recursive least square method based on the traditional one is presented to determine the TV coefficient matrices of the multivariate parametric model established in the proposed method. The efficiency and accuracy of the identification approach is demonstrated by a numerical example, in which a TV mass-string system with three-degree-of-freedom is investigated. Satisfied results are obtained.


2016 ◽  
Vol 744 ◽  
pp. 012170 ◽  
Author(s):  
Jose M. Soria ◽  
Ivan M. Díaz ◽  
Emiliano Pereira ◽  
Jaime H. García-Palacios ◽  
Xidong Wang

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