An Improved Stochastic Configuration Networks With Compact Structure and Parameter Adaptation
Stochastic Configuration Networks (SCNs) perform well in machine learning and data mining tasks in complex data environments. However, traditional SCNs have limitations in network size and computation time. To address these issues, this paper proposes an improved version of SCNs. There are two key i...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10852165/ |
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| author | Sanyi Li Hongyu Guan Peng Liu Weichao Yue Qian Wang |
| author_facet | Sanyi Li Hongyu Guan Peng Liu Weichao Yue Qian Wang |
| author_sort | Sanyi Li |
| collection | DOAJ |
| description | Stochastic Configuration Networks (SCNs) perform well in machine learning and data mining tasks in complex data environments. However, traditional SCNs have limitations in network size and computation time. To address these issues, this paper proposes an improved version of SCNs. There are two key improvements: First, the stopping condition for generating neurons is optimized to improve the effectiveness of new neurons. Second, the regularization parameter r is adjusted dynamically to speed up the learning process. These improvements are trying to increase the efficiency of SCN construction, reduce the number of redundant neurons, and shorten the overall computation time. Experiments comparing this method with existing ones show that the proposed approach not only reduces network complexity but also effectively decreases training time. In addition, experimental results using baseline datasets and UCI databases show that the number of nodes required for oscn is reduced by approximately 50% and the computation time is reduced by approximately 40% compared to traditional algorithms. |
| format | Article |
| id | doaj-art-3d665745c8ec42bdad9a914c17385500 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-3d665745c8ec42bdad9a914c173855002025-01-31T00:01:26ZengIEEEIEEE Access2169-35362025-01-0113181411815110.1109/ACCESS.2025.353355510852165An Improved Stochastic Configuration Networks With Compact Structure and Parameter AdaptationSanyi Li0https://orcid.org/0009-0009-0373-7562Hongyu Guan1Peng Liu2Weichao Yue3https://orcid.org/0000-0002-2346-0216Qian Wang4https://orcid.org/0000-0001-9525-8124School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, ChinaSchool of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, ChinaSchool of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, ChinaSchool of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, ChinaSchool of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, ChinaStochastic Configuration Networks (SCNs) perform well in machine learning and data mining tasks in complex data environments. However, traditional SCNs have limitations in network size and computation time. To address these issues, this paper proposes an improved version of SCNs. There are two key improvements: First, the stopping condition for generating neurons is optimized to improve the effectiveness of new neurons. Second, the regularization parameter r is adjusted dynamically to speed up the learning process. These improvements are trying to increase the efficiency of SCN construction, reduce the number of redundant neurons, and shorten the overall computation time. Experiments comparing this method with existing ones show that the proposed approach not only reduces network complexity but also effectively decreases training time. In addition, experimental results using baseline datasets and UCI databases show that the number of nodes required for oscn is reduced by approximately 50% and the computation time is reduced by approximately 40% compared to traditional algorithms.https://ieeexplore.ieee.org/document/10852165/Stochastic configuration networksparameter optimizationmemory efficiencydynamic regularizationtraining efficiency |
| spellingShingle | Sanyi Li Hongyu Guan Peng Liu Weichao Yue Qian Wang An Improved Stochastic Configuration Networks With Compact Structure and Parameter Adaptation IEEE Access Stochastic configuration networks parameter optimization memory efficiency dynamic regularization training efficiency |
| title | An Improved Stochastic Configuration Networks With Compact Structure and Parameter Adaptation |
| title_full | An Improved Stochastic Configuration Networks With Compact Structure and Parameter Adaptation |
| title_fullStr | An Improved Stochastic Configuration Networks With Compact Structure and Parameter Adaptation |
| title_full_unstemmed | An Improved Stochastic Configuration Networks With Compact Structure and Parameter Adaptation |
| title_short | An Improved Stochastic Configuration Networks With Compact Structure and Parameter Adaptation |
| title_sort | improved stochastic configuration networks with compact structure and parameter adaptation |
| topic | Stochastic configuration networks parameter optimization memory efficiency dynamic regularization training efficiency |
| url | https://ieeexplore.ieee.org/document/10852165/ |
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