ECheynet-earthquakeSim-v2.1-0-g390b069
所属分类：matlab编程
开发工具：matlab
文件大小：87KB
下载次数：0
上传日期：2020-05-13 20:30:15
上 传 者：
程985
说明： 拟合过程（lsqcurvefit）需要优化工具箱。但是，也可以使用其他功能。这里通过filtfilt函数使用信号处理工具箱。任何意见，问题或建议，以改善守则是热烈欢迎。
(The fitting process (lsqcurvefit) requires an optimization toolbox. However, other functions are available. Here we use the signal processing toolbox through the filtfilterfilt function. Any comments, questions or suggestions to improve the code are warmly welcomed.)
文件列表:
ECheynet-earthquakeSim-390b069 (0, 2020-03-23)
ECheynet-earthquakeSim-390b069\Example.mlx (84442, 2020-03-23)
ECheynet-earthquakeSim-390b069\LICENSE (1519, 2020-03-23)
ECheynet-earthquakeSim-390b069\fitKT.m (4566, 2020-03-23)
ECheynet-earthquakeSim-390b069\seismSim.m (2703, 2020-03-23)
# earthquakeSim
Ground acceleration records are simulated using the non-stationnary Kanai–Tajimi model
[](https://se.mathworks.com/matlabcentral/fileexchange/56701-earthquake-simulation)
Ground acceleration records are simulated using the non-stationary Kanai–Tajimi model
Non-stationary ground acceleration records are simulated based on the example proposed in the paper of Guo et al. [3] that I have found well explained. However, the method itself is older, see e.g. [1, 2]. The present submission contains, in addition, a Matlab function to fit the non-stationary Kanai–Tajimi model to ground acceleration records.
The optimization toolbox is required for the fitting procedure (lsqcurvefit). However, other functions may alternatively be used. The signal processing toolbox is used here through the function filtfilt. Any comment, question or suggestions to improve the code is warmly welcomed.
References
[1] Lin, Y. K., & Yong, Y. (1***7). Evolutionary Kanai-Tajimi earthquake models. Journal of engineering mechanics, 113(8), 1119-1137.
[2] Rofooei, F. R., Mobarake, A., & Ahmadi, G. (2001). Generation of artificial earthquake records with a nonstationary Kanai–Tajimi model. Engineering Structures, 23(7), 827-837.
[3] Guo, Y., & Kareem, A. (2016). System identification through nonstationary data using Time-Frequency Blind Source Separation. Journal of Sound and Vibration, 371, 110-131.
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