A novel recursive modal parameter estimator for operational time-varying structural dynamic systems based on least squares support vector machine and time series model

2020 ◽  
Vol 229 ◽  
pp. 106173 ◽  
Author(s):  
Jie Kang ◽  
Li Liu ◽  
Si-Da Zhou ◽  
Da-Yu Wang ◽  
Yuan-Chen Ma
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Least Squares ◽  
Dynamic Systems ◽  
Support Vector ◽  
Time Varying ◽  
Modal Parameter ◽  
Parameter Estimator ◽  
Structural Dynamic ◽  
Operational Time

Study on Prediction of Chaotic Time Series Using Least Squares Support Vector Machines

2014 ◽  
Vol 1061-1062 ◽  
pp. 935-938
Author(s):  
Xin You Wang ◽  
Guo Fei Gao ◽  
Zhan Qu ◽  
Hai Feng Pu
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Support Vector Machines ◽  
Least Squares ◽  
Prediction Accuracy ◽  
Chaotic Time Series ◽  
Support Vector ◽  
Vector Machines ◽  
Online Prediction ◽  
Better Than

The predictions of chaotic time series by applying the least squares support vector machine (LS-SVM), with comparison with the traditional-SVM and-SVM, were specified. The results show that, compared with the traditional SVM, the prediction accuracy of LS-SVM is better than the traditional SVM and more suitable for time series online prediction.

scholarly journals Ship Accident Prediction Based on Improved Quantum-Behaved PSO-LSSVM

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Tian Chai ◽  
Han Xue ◽  
Kaibiao Sun ◽  
Jinxian Weng
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Least Squares ◽  
Traffic Safety ◽  
Management Strategy ◽  
Support Vector ◽  
Important Indicator ◽  
Accident Prediction ◽  
Processing Operation ◽  
Water Transportation

Water transportation plays an important role in the comprehensive transportation system and regional logistics. The number of vessel accidents is an important indicator for evaluating vessel traffic safety and the efficiency of the maritime management strategy. The aim of this work is to provide an efficient way to predict the number of vessel accidents in China. Firstly, to weaken the randomness of the vessel accident number time series, the gray processing operation is adopted to generate a new sequence with exponential and approximate exponential rules. In addition, an extended least-squares support vector machine (LSSVM) model is applied in the forecasting of the new sequence, in which the parameters of the LSSVM are optimized by an improved quantum-behaved particle swarm (IQPSO). The proposed method is applied in the forecasting of the number of vessel accidents in China, and the efficiency is shown by comparing the prediction results with GM (1, 1), PSO-LSSVM, and QPSO-LSSVM.

scholarly journals Crack Prediction Based on Wavelet Correlation Analysis Least Squares Support Vector Machine for Stone Cultural Relics

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Bao Liu ◽  
Fei Ye ◽  
Kun Mu ◽  
Jingting Wang ◽  
Jinyu Zhang
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Correlation Analysis ◽  
Environmental Monitoring ◽  
Least Squares ◽  
Multivariate Time Series ◽  
Shaanxi Province ◽  
Support Vector ◽  
Archaeological Heritage ◽  
Cultural Relics

Preventive protection of cultural relics is to make use of all the science and technology beneficial to the research and protection of archaeological heritage to predict the disease of cultural relics. The existing preventive cultural relics protection system has made some achievements in environmental monitoring, but the analysis and utilization of large data of cultural relics are still insufficient. In this paper, under the idea of multisource information fusion, a least squares support vector machine regression method based on multivariate time series wavelet correlation analysis is proposed to achieve accurate crack prediction of stone cultural relics. Firstly, the correlation of multivariate time series of stone cultural relics are quantitatively analyzed and the validity of characteristic variables of the crack is discriminated by wavelet correlation analysis; then, a least squares support vector machine prediction model is constructed based on the correlation obtained from the analysis; finally, the good performance of the method is verified by using the environmental monitoring data of the rock mass fracture in the North Qianfo Cliff of Dafo Temple in Binzhou City of Shaanxi Province. The experimental results show that the proposed method is more effective than the traditional backpropagation neural network, support vector machine, and relevance vector machine regression methods. This method is universal and easy to implement for multisource data prediction of nonmovable cultural relics diseases. It provides a scientific theoretical reference for the preventive protection of cultural relics.

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