Fuzzy stochastic neural network model for structural system identification
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- 作者：Xiaomo Jianga ; jiang.xiaomo@gmail.com" class="auth_mail" title="E-mail the corresponding author ; Sankaran Mahadevanb ; sankaran.mahadevan@vanderbilt.edu" class="auth_mail" title="E-mail the corresponding author ; Yong Yuana ; yuany@tongji.edu.cn" class="auth_mail" title="E-mail the corresponding author
- 关键词：Bayesian analysis ; System identification ; Stochastic models ; Neural networks ; Damage assessment
- 刊名：Mechanical Systems and Signal Processing
- 出版年：2017
- 出版时间：1 January 2017
- 年：2017
- 卷：82
- 期：Complete
- 页码：394-411
- 全文大小：1585 K
文摘
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Dynamic fuzzy stochastic neural network for structural system identification.
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Bayesian method and false nearest neighbor approach for model optimization.
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Natural gradient method with Kullback–Leibler distance criterion for model training.
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Pseudospectrum and Bayesian hypothesis testing methods for model validation.
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Address both fuzzy information and measurement uncertainties in ambient data.