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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2511.03743 |
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| _version_ | 1866915601506631680 |
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| author | Impraimakis, Marios |
| author_facet | Impraimakis, Marios |
| contents | The response-only model class selection capability of a novel deep convolutional neural network method is examined herein in a simple, yet effective, manner. Specifically, the responses from a unique degree of freedom along with their class information train and validate a one-dimensional convolutional neural network. In doing so, the network selects the model class of new and unlabeled signals without the need of the system input information, or full system identification. An optional physics-based algorithm enhancement is also examined using the Kalman filter to fuse the system response signals using the kinematics constraints of the acceleration and displacement data. Importantly, the method is shown to select the model class in slight signal variations attributed to the damping behavior or hysteresis behavior on both linear and nonlinear dynamic systems, as well as on a 3D building finite element model, providing a powerful tool for structural health monitoring applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_03743 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A convolutional neural network deep learning method for model class selection Impraimakis, Marios Systems and Control Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Signal Processing 68T05 (Learning and adaptive systems) 93C95 (Neural networks in control theory) I.2.6; I.2.8 The response-only model class selection capability of a novel deep convolutional neural network method is examined herein in a simple, yet effective, manner. Specifically, the responses from a unique degree of freedom along with their class information train and validate a one-dimensional convolutional neural network. In doing so, the network selects the model class of new and unlabeled signals without the need of the system input information, or full system identification. An optional physics-based algorithm enhancement is also examined using the Kalman filter to fuse the system response signals using the kinematics constraints of the acceleration and displacement data. Importantly, the method is shown to select the model class in slight signal variations attributed to the damping behavior or hysteresis behavior on both linear and nonlinear dynamic systems, as well as on a 3D building finite element model, providing a powerful tool for structural health monitoring applications. |
| title | A convolutional neural network deep learning method for model class selection |
| topic | Systems and Control Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Signal Processing 68T05 (Learning and adaptive systems) 93C95 (Neural networks in control theory) I.2.6; I.2.8 |
| url | https://arxiv.org/abs/2511.03743 |