Adaptive Soft Sensors for Non-Gaussian Chemical Process Plant Data Based on Locally Weighted Partial Least Square
This thesis proposes an improved algorithm attributed to its abilities to deal with non-Gaussian distributed and nonlinear data and missing measurements. It was formulated through a modification on locally weighted partial least square by incorporating an ensemble method, Kernel function and indepen...
| Main Author: | |
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| Format: | Thesis |
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Curtin University
2019
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| Online Access: | http://hdl.handle.net/20.500.11937/77028 |
| _version_ | 1848763806180704256 |
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| author | Yeo, Wan Sieng |
| author_facet | Yeo, Wan Sieng |
| author_sort | Yeo, Wan Sieng |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This thesis proposes an improved algorithm attributed to its abilities to deal with non-Gaussian distributed and nonlinear data and missing measurements. It was formulated through a modification on locally weighted partial least square by incorporating an ensemble method, Kernel function and independent component analysis and expectation maximisation algorithms. The algorithm was then tested using process data generated from six simulated plants. Simulation results indicate superiority of this algorithm compared to the existing algorithms. |
| first_indexed | 2025-11-14T11:09:19Z |
| format | Thesis |
| id | curtin-20.500.11937-77028 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:09:19Z |
| publishDate | 2019 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-770282021-11-26T00:06:46Z Adaptive Soft Sensors for Non-Gaussian Chemical Process Plant Data Based on Locally Weighted Partial Least Square Yeo, Wan Sieng This thesis proposes an improved algorithm attributed to its abilities to deal with non-Gaussian distributed and nonlinear data and missing measurements. It was formulated through a modification on locally weighted partial least square by incorporating an ensemble method, Kernel function and independent component analysis and expectation maximisation algorithms. The algorithm was then tested using process data generated from six simulated plants. Simulation results indicate superiority of this algorithm compared to the existing algorithms. 2019 Thesis http://hdl.handle.net/20.500.11937/77028 Curtin University fulltext |
| spellingShingle | Yeo, Wan Sieng Adaptive Soft Sensors for Non-Gaussian Chemical Process Plant Data Based on Locally Weighted Partial Least Square |
| title | Adaptive Soft Sensors for Non-Gaussian Chemical Process Plant Data Based on Locally Weighted Partial Least Square |
| title_full | Adaptive Soft Sensors for Non-Gaussian Chemical Process Plant Data Based on Locally Weighted Partial Least Square |
| title_fullStr | Adaptive Soft Sensors for Non-Gaussian Chemical Process Plant Data Based on Locally Weighted Partial Least Square |
| title_full_unstemmed | Adaptive Soft Sensors for Non-Gaussian Chemical Process Plant Data Based on Locally Weighted Partial Least Square |
| title_short | Adaptive Soft Sensors for Non-Gaussian Chemical Process Plant Data Based on Locally Weighted Partial Least Square |
| title_sort | adaptive soft sensors for non-gaussian chemical process plant data based on locally weighted partial least square |
| url | http://hdl.handle.net/20.500.11937/77028 |