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Anastasios Chassiakos
Researcher at California State University, Long Beach
Publications - 40
Citations - 3713
Anastasios Chassiakos is an academic researcher from California State University, Long Beach. The author has contributed to research in topics: Nonlinear system & Artificial neural network. The author has an hindex of 19, co-authored 40 publications receiving 3494 citations. Previous affiliations of Anastasios Chassiakos include California State University & University of Southern California.
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Journal ArticleDOI
Structural control: past, present, and future
George W. Housner,Lawrence A. Bergman,Thomas K. Caughey,Anastasios Chassiakos,R. O. Claus,Sami F. Masri,Robert E. Skelton,T. T. Soong,Billie F. Spencer,J. T. P. Yao +9 more
TL;DR: In this paper, the authors provide a concise point of departure for researchers and practitioners alike wishing to assess the current state of the art in the control and monitoring of civil engineering structures, and provide a link between structural control and other fields of control theory.
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Application of Neural Networks for Detection of Changes in Nonlinear Systems
TL;DR: In this article, a nonparametric structural damage detection methodology based on nonlinear system identification approaches is presented for the health monitoring of structure-unknown systems, which relies on the use of vibration measurements from a healthy system to train a neural network for identification purposes.
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Identification of Nonlinear Dynamic Systems Using Neural Networks
TL;DR: In this article, the authors explore the potential of using parallel distributed processing methodologies (artificial neural networks) to identify the internal forces of structure unknown non linear dynamic systems, and explore the use of neural networks to predict the internal dynamics of non-linear dynamic systems.
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On-Line Parametric Identification of MDOF Nonlinear Hysteretic Systems
TL;DR: In this paper, a method based on adaptive estimation approaches is presented for the on-line identification of hysteretic systems under arbitrary dynamic environments, where no information is available on the system parameters, even the mass distribution.
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Neural Network Approach to Detection of Changes in Structural Parameters
TL;DR: In this article, a neural network-based approach is presented for the detection of changes in the characteristics of structure-unknown systems, which relies on the use of vibration measurements from a "healthy" system to train a CNN for identification purposes.