Unsupervised Regression Model of Geodesic Flow Kernel Based on Dynamic Independent Component Analysis and Dynamic Principal Component Analysis
It is difficult to accurately measure parameters by using the traditional soft sensor algorithm when the working condition of industrial process is changed. Therefore, a transfer learning strategy is introduced based on geodesic flow kernel to solve this problem. At the same time, the method is opti...
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| Autores principales: | , , , |
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| Formato: | article |
| Lenguaje: | ZH |
| Publicado: |
Editorial Office of Journal of Shanghai Jiao Tong University
2020
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/a55168d826a44c0ab572a2137dbea08a |
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| Sumario: | It is difficult to accurately measure parameters by using the traditional soft sensor algorithm when the working condition of industrial process is changed. Therefore, a transfer learning strategy is introduced based on geodesic flow kernel to solve this problem. At the same time, the method is optimized to solve the problems of dynamic characteristic extraction and incomplete Gaussian distribution in industrial process. The augmented matrix is first constructed to deal with the dynamic characteristics of the process. Independent component analysis and principal component analysis are performed on the processed data to extract the non-Gaussian and Gaussian information of the source domain and the target domain. Then, the non-Gaussian and Gaussian information of the source domain is adapted to the target domain respectively on the Grassmann manifold. Finally, the maximum mean discrepancy is used to measure the distribution between the source domain and the target domain after domain adaptation, and the calculated results are applied to construct the weight of the model based on the source domain after domain adaptation. The results show that the method not only reduces the difference of distribution between the source domain and the target domain, but also solves the problems of dynamic characteristic extraction and incomplete Gaussian distribution in industrial process. The validity and the practicability of the model are proved by experiments on Tennessee Eastman data. |
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