A multiobjective prediction model with incremental learning ability by developing a multi-source filter neural network for the electrolytic aluminium process
Improving current efficiency and reducing energy consumption are two important technical goals of the electrolytic aluminum process (EAP). However, because the process involves complex noise characteristics (i.e., unknown types, redundant distributions and variable forms), it is very difficult to ac...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
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2022
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| Online Access: | https://hdl.handle.net/10356/163863 |
| _version_ | 1811680562993168384 |
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| author | Yao, Lizhong Ding, Wei He, Tiantian Liu, Shouxin Nie, Ling |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Yao, Lizhong Ding, Wei He, Tiantian Liu, Shouxin Nie, Ling |
| author_sort | Yao, Lizhong |
| collection | NTU |
| description | Improving current efficiency and reducing energy consumption are two important technical goals of the electrolytic aluminum process (EAP). However, because the process involves complex noise characteristics (i.e., unknown types, redundant distributions and variable forms), it is very difficult to accurately develop a multiobjective prediction model. To overcome this problem, in this paper, a novel framework of multiobjective incremental learning based on a multi-source filter neural network (MSFNN) is presented. The proposed framework first presents a “multi-source filter” (MSF) technique that utilizes the mean and variance in the unscented Kalman filter (UKF) to guide the importance function of the particle filter (PF) based on a density kernel estimation method. Then, the MSF is embedded in the mutated neural network to adjust weights in real time. Third, weights are calculated and normalized by a modified importance function, which is the basis for further optimizing a secondary sampling based on sampling importance resampling (SIR). Finally, the incremental learning model with two objectives (i.e., process power consumption and current efficiency) based on the MSFNN in the EAP is established. The presented framework has been verified by the real-world EAP and some closely related methods. All test results indicate that the MSFNN’s relative prediction errors of the above two objectives are controlled within 0.51% and 0.38%, respectively and prove that MSFNN has significant competitive advantages over other recent filtering network models. Successfully establishment of the proposed framework provides a model foundation for multiobjective optimization problems in the EAP. |
| first_indexed | 2024-10-01T03:27:02Z |
| format | Journal Article |
| id | ntu-10356/163863 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:27:02Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-10356/1638632022-12-20T07:58:13Z A multiobjective prediction model with incremental learning ability by developing a multi-source filter neural network for the electrolytic aluminium process Yao, Lizhong Ding, Wei He, Tiantian Liu, Shouxin Nie, Ling School of Computer Science and Engineering Engineering::Computer science and engineering Multi-Source Filter Unscented Kalman Filter Improving current efficiency and reducing energy consumption are two important technical goals of the electrolytic aluminum process (EAP). However, because the process involves complex noise characteristics (i.e., unknown types, redundant distributions and variable forms), it is very difficult to accurately develop a multiobjective prediction model. To overcome this problem, in this paper, a novel framework of multiobjective incremental learning based on a multi-source filter neural network (MSFNN) is presented. The proposed framework first presents a “multi-source filter” (MSF) technique that utilizes the mean and variance in the unscented Kalman filter (UKF) to guide the importance function of the particle filter (PF) based on a density kernel estimation method. Then, the MSF is embedded in the mutated neural network to adjust weights in real time. Third, weights are calculated and normalized by a modified importance function, which is the basis for further optimizing a secondary sampling based on sampling importance resampling (SIR). Finally, the incremental learning model with two objectives (i.e., process power consumption and current efficiency) based on the MSFNN in the EAP is established. The presented framework has been verified by the real-world EAP and some closely related methods. All test results indicate that the MSFNN’s relative prediction errors of the above two objectives are controlled within 0.51% and 0.38%, respectively and prove that MSFNN has significant competitive advantages over other recent filtering network models. Successfully establishment of the proposed framework provides a model foundation for multiobjective optimization problems in the EAP. Published version This study was supported by the National Natural Science Foundation of China (No. 51805059 and 61802317), and Scientific and Technological Research Program of Chongqing Municipal Education Commission (KJQN2021033). 2022-12-20T07:58:13Z 2022-12-20T07:58:13Z 2022 Journal Article Yao, L., Ding, W., He, T., Liu, S. & Nie, L. (2022). A multiobjective prediction model with incremental learning ability by developing a multi-source filter neural network for the electrolytic aluminium process. Applied Intelligence, 52(15), 17387-17409. https://dx.doi.org/10.1007/s10489-022-03314-9 0924-669X https://hdl.handle.net/10356/163863 10.1007/s10489-022-03314-9 2-s2.0-85127599640 15 52 17387 17409 en Applied Intelligence © The Author(s) 2022. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons. org/licenses/by/4.0/. application/pdf |
| spellingShingle | Engineering::Computer science and engineering Multi-Source Filter Unscented Kalman Filter Yao, Lizhong Ding, Wei He, Tiantian Liu, Shouxin Nie, Ling A multiobjective prediction model with incremental learning ability by developing a multi-source filter neural network for the electrolytic aluminium process |
| title | A multiobjective prediction model with incremental learning ability by developing a multi-source filter neural network for the electrolytic aluminium process |
| title_full | A multiobjective prediction model with incremental learning ability by developing a multi-source filter neural network for the electrolytic aluminium process |
| title_fullStr | A multiobjective prediction model with incremental learning ability by developing a multi-source filter neural network for the electrolytic aluminium process |
| title_full_unstemmed | A multiobjective prediction model with incremental learning ability by developing a multi-source filter neural network for the electrolytic aluminium process |
| title_short | A multiobjective prediction model with incremental learning ability by developing a multi-source filter neural network for the electrolytic aluminium process |
| title_sort | multiobjective prediction model with incremental learning ability by developing a multi source filter neural network for the electrolytic aluminium process |
| topic | Engineering::Computer science and engineering Multi-Source Filter Unscented Kalman Filter |
| url | https://hdl.handle.net/10356/163863 |
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