System identification of dynamic structure by the multi-branch BPNN

Li, Hong‐Nan; Yang, Hao

doi:10.1016/j.neucom.2006.10.015

Cited by 11 publications

(3 citation statements)

References 14 publications

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“…For instance, a dynamic time-delay fuzzy wavelet ANN model was developed to (1) create a PR model that captures the time series data accurately and efficiently and (2) handle two types of imprecision in the measured data: fuzzy information and measurement uncertainties (Jiang and Adeli, 2005; Jiang et al, 2017). A multi-branch ANN model that separates the structural state variables and seismic inputs was developed to identify a frame structure (Li and Yang, 2007). An intelligent neural system, which combines competitive ANNs and radial basis function ANNs, was developed to enhance accuracy, generality, and speed (Gholizadeh et al, 2009).…”

Section: System Identification and Damage Detectionmentioning

confidence: 99%

The promise of implementing machine learning in earthquake engineering: A state-of-the-art review

et al. 2020

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Machine learning (ML) has evolved rapidly over recent years with the promise to substantially alter and enhance the role of data science in a variety of disciplines. Compared with traditional approaches, ML offers advantages to handle complex problems, provide computational efficiency, propagate and treat uncertainties, and facilitate decision making. Also, the maturing of ML has led to significant advances in not only the main-stream artificial intelligence (AI) research but also other science and engineering fields, such as material science, bioengineering, construction management, and transportation engineering. This study conducts a comprehensive review of the progress and challenges of implementing ML in the earthquake engineering domain. A hierarchical attribute matrix is adopted to categorize the existing literature based on four traits identified in the field, such as ML method, topic area, data resource, and scale of analysis. The state-of-the-art review indicates to what extent ML has been applied in four topic areas of earthquake engineering, including seismic hazard analysis, system identification and damage detection, seismic fragility assessment, and structural control for earthquake mitigation. Moreover, research challenges and the associated future research needs are discussed, which include embracing the next generation of data sharing and sensor technologies, implementing more advanced ML techniques, and developing physics-guided ML models.

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Section: System Identification and Damage Detectionmentioning

confidence: 99%

The promise of implementing machine learning in earthquake engineering: A state-of-the-art review

et al. 2020

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“…We make use of optimized gradient descent method to adjust the neural network weights, this method adjusts the parameters along the opposite direction of the error-performance function gradient until the error of the desired network output and actual network output satisfies the performance index required [34][35][36][37]. In general, BP neural network consists of input layer, hidden layer and output layer.…”

Section: Bp Neural Network Based Constrained Controller Designmentioning

confidence: 99%

A novel adaptive neural network constrained control for solid oxide fuel cells via dynamic anti-windup

Liu

2016

Neurocomputing

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“…Modal parameter identification can provide references for dynamic response prediction (Oh et al, 2017;Li and Yang, 2007), fault diagnosis (Wang and Qi, 2016;Chandra and Sekhar, 2016), safety evaluation (Mao et al, 2019;Herman and Peeters, 2003), and structural optimization (Zhu et al, 2019c;Jung et al, 2015;Zhou et al, 2022b). As one of the famous parameter identification methods in time domain, the subspace identification method has been widely used in the fields of aerospace, mechanical, and civil engineering (Reynders, 2012;Zhu et al, 2019aZhu et al, , 2019bXu et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Automatic modal identification based on similarity filtering and fuzzy clustering

Jiang

Wang

et al. 2023

Journal of Vibration and Control

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Artificially determining the model order is necessary for time domain modal identification, which affects the identification efficiency and accuracy. An automatic identification method is proposed to determine the best estimation of the model order. Based on the covariance-driven stochastic subspace identification method (SSI-COV), the singular value decomposition of the Toeplitz matrix is carried out to determine the range of the lower triangular matrix. The similarity coefficient and distance function are introduced to cluster the modes, and the poles of the false modes are removed to obtain the clustering stabilization diagram. The model order is taken as the number of clustering centers of the fuzzy c-means (FCM) algorithm, and all the poles of the stabilization diagram are clustered to get the modal parameters of each order. The effectiveness of the proposed method is verified by adopting a thin-walled cylindrical column, a triangular-prism truss in simulation, and a flexible beam in experiment. The robustness of the method is illustrated by adding white noise to the simulated response data. The advantage of the clustering stabilization diagram is demonstrated by comparing with the traditional stabilization diagram.

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System identification of dynamic structure by the multi-branch BPNN

Cited by 11 publications

References 14 publications

The promise of implementing machine learning in earthquake engineering: A state-of-the-art review

The promise of implementing machine learning in earthquake engineering: A state-of-the-art review

A novel adaptive neural network constrained control for solid oxide fuel cells via dynamic anti-windup

Automatic modal identification based on similarity filtering and fuzzy clustering

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