Generalized YAST algorithm for signal subspace tracking

Arjomandi-Lari, Mostafa; Karimi, Mahmood

doi:10.1016/j.sigpro.2015.04.025

Cited by 17 publications

(6 citation statements)

References 33 publications

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“…e strong earthquake records data used to support the findings of this study may be released upon application to the Center for Engineering Strong Motion Data at https:// strongmotioncenter.org. e MATLAB code for the GYAST algorithm online to support the findings of this study is available from the Professor Mostafa Arjomandi-Lari [22]. e numerical simulation and other data used to support the findings of this study are available from the corresponding author upon request.…”

Section: Data Availabilitymentioning

confidence: 99%

“…In this work, the RSSI method is used to extract the modal parameters of concrete dams based on the strongmotion records of the structure. e online version eigenvalue decomposition (EVD) is solved as a subspace tracking problem using the generalized yet another subspace tracker (GYAST) algorithm [22]. GYAST is a newly proposed advanced algorithm which makes several robust modifications of the original subspace tracking algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Online Modal Identification of Concrete Dams Using the Subspace Tracking‐Based Method

Cheng

Tong

Zheng

2019

Shock and Vibration

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To investigate the time-varying dynamic characteristics of concrete dams under the excitation of large earthquakes for online structural health monitoring and damage evaluation, an online modal identification procedure based on strong-motion records is proposed. The online modal identification of concrete dams is expressed as a subspace tracking problem, and a newly developed recursive stochastic subspace identification (RSSI) method based on the generalized yet another subspace tracker (GYAST) algorithm, which exploits both the accuracy of the subspace identification and fast computational capability, is used to extract the time-varying modal parameters of concrete dams during earthquakes. With the simulated vibration response records, a numerical example is used to verify the accuracy, robustness, and efficiency of the proposed GYAST-based, time-varying modal identification method. Then, the realistic strong-motion records of the Pacoima arch dam are analysed using the proposed modal identification procedure, and the time-varying characteristics of the concrete arch dam during three different earthquakes are analysed.

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Section: Data Availabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Online Modal Identification of Concrete Dams Using the Subspace Tracking‐Based Method

Cheng

Tong

Zheng

2019

Shock and Vibration

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“…In the 2000's we have FAPI [15] and FDPM [16], both of which are very efficient subspace tracker with only a single parameter to be set. More recently we have YAST [17] and its improved version [18].…”

Section: A Literature Reviewmentioning

confidence: 99%

An experiment on subspace learning for Echo State Network

Yuenyong

2016

2016 7th International Conference of Information and Communication Technology for Embedded Systems (IC-ICTES)

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Echo State Network (ESN) is a type of neural network with convex training used as nonlinear adaptive filters. It requires relative large network size and O(N 2 ) training algorithm in order to realize its full potential. We present a subspace training scheme for ESN that reduces the training cost from O(N 2 ) to O(NL) where L < N is the dimension of the subspace.Experiments show that L = 20 is sufficient for networks of size N = 100 and that the performance of subspace training algorithm is actually better than current standard training algorithm while having only 20% computational cost and preserving convex training.

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“…Because the computational efficiency of SVD methods is affected obviously by the system order, a series of recursive identification algorithms based on the theory of signal subspace projection had been proposed. 18–21 Among these algorithms, the most representative approach is the projection approximation subspace tracking (PAST) algorithm. 22 Signal subspace iteration is applied in these methods (hereafter called the PAST series methods) instead of the classical SVD to track the time-varying signal subspace matrix from the I-O data.…”

Section: Introductionmentioning

confidence: 99%

Identification of the time-varying modal parameters of a spacecraft with flexible appendages using a recursive predictor-based subspace identification algorithm

Liu

2018

Proceedings of the Institution of Mechanical Engineers, Part G:

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This study focuses on the recursive identification of the time-varying modal parameters of on-orbit spacecraft caused by structural configuration changes. For this purpose, an algorithm called recursive predictor-based subspace identification is applied as an alternative method to improve the computational efficiency and noise robustness, and to implement an online identification of system parameters. In the existing time-domain identification methods, the eigensystem realization algorithm and subspace identification methods are usually applied to obtain the on-orbit spacecraft modal parameters. However, these approaches are designed based on a time-invariant system and singular value decomposition, which require a significant amount of computational time. Thus, these methods are difficult to employ for online identification. According to the adaptive filter theory, the recursive predictor-based subspace identification algorithm can not only avoid the singular value decomposition computation but also provide unbiased estimates in a general noisy framework using the recursive least squares approach. Furthermore, in comparison with the classical projection approximation subspace tracking series recursive algorithm, the recursive predictor-based subspace identification method is more suitable for systems with strong noise disturbances. By establishing the dynamics model of a large rigid-flexible coupling spacecraft, three cases of on-orbit modal parameter variation with time are investigated, and the corresponding system frequencies are identified using the recursive predictor-based subspace identification, projection approximation subspace tracking, and singular value decomposition methods. The results demonstrate that the recursive predictor-based subspace identification algorithm can be used to effectively perform an online parameter identification, and the corresponding computational efficiency and noise robustness are better than those of the singular value decomposition and projection approximation subspace tracking series approaches, respectively. Finally, the applicability of this method is also verified through a numerical simulation.

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Generalized YAST algorithm for signal subspace tracking

Cited by 17 publications

References 33 publications

Online Modal Identification of Concrete Dams Using the Subspace Tracking‐Based Method

Online Modal Identification of Concrete Dams Using the Subspace Tracking‐Based Method

An experiment on subspace learning for Echo State Network

Identification of the time-varying modal parameters of a spacecraft with flexible appendages using a recursive predictor-based subspace identification algorithm

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