Solving Time-Varying Complex-Valued Sylvester Equation via Adaptive Coefficient and Non-Convex Projection Zeroing Neural Network
The time-varying complex-valued Sylvester equation (TVCVSE) often appears in many fields such as control and communication engineering. Classical recurrent neural network (RNN) models (e.g., gradient neural network (GNN) and zeroing neural network (ZNN)) are often used to solve such problems. This p...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9551969/ |
| _version_ | 1818027670507618304 |
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| author | Jiahao Wu Chengze Jiang Baitao Chen Qixiang Mei Xiuchun Xiao |
| author_facet | Jiahao Wu Chengze Jiang Baitao Chen Qixiang Mei Xiuchun Xiao |
| author_sort | Jiahao Wu |
| collection | DOAJ |
| description | The time-varying complex-valued Sylvester equation (TVCVSE) often appears in many fields such as control and communication engineering. Classical recurrent neural network (RNN) models (e.g., gradient neural network (GNN) and zeroing neural network (ZNN)) are often used to solve such problems. This paper proposes an adaptive coefficient and non-convex projection zeroing neural network (ACNPZNN) model for solving TVCVSE. To enhance its adaptability as residual error decreasing as time, an adaptive coefficient is designed based on residual error. Meanwhile, this paper breaks the convex constraint by constructing two complex-valued non-convex projection activation functions from two different aspects. Moreover, the global convergence of the proposed model is proved, the anti-noise performance of the ACNPZNN model under different noises is theoretically analyzed. Finally, simulation experiments are provided to compare the convergence performance of different models, which simultaneously verifies the effectiveness and superiority of the proposed model. |
| first_indexed | 2024-12-10T04:51:35Z |
| format | Article |
| id | doaj.art-d1936313c387453bb67ed288946ca6ab |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-10T04:51:35Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d1936313c387453bb67ed288946ca6ab2022-12-22T02:01:37ZengIEEEIEEE Access2169-35362021-01-01913589013589810.1109/ACCESS.2021.31161529551969Solving Time-Varying Complex-Valued Sylvester Equation via Adaptive Coefficient and Non-Convex Projection Zeroing Neural NetworkJiahao Wu0https://orcid.org/0000-0003-2053-562XChengze Jiang1Baitao Chen2https://orcid.org/0000-0001-5625-4614Qixiang Mei3Xiuchun Xiao4https://orcid.org/0000-0002-3389-6689School of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang, ChinaSchool of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang, ChinaEducation Quality Monitoring and Evaluation Center, Guangdong Ocean University, Zhanjiang, ChinaSchool of Mathematics and Computer, Guangdong Ocean University, Zhanjiang, ChinaSchool of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang, ChinaThe time-varying complex-valued Sylvester equation (TVCVSE) often appears in many fields such as control and communication engineering. Classical recurrent neural network (RNN) models (e.g., gradient neural network (GNN) and zeroing neural network (ZNN)) are often used to solve such problems. This paper proposes an adaptive coefficient and non-convex projection zeroing neural network (ACNPZNN) model for solving TVCVSE. To enhance its adaptability as residual error decreasing as time, an adaptive coefficient is designed based on residual error. Meanwhile, this paper breaks the convex constraint by constructing two complex-valued non-convex projection activation functions from two different aspects. Moreover, the global convergence of the proposed model is proved, the anti-noise performance of the ACNPZNN model under different noises is theoretically analyzed. Finally, simulation experiments are provided to compare the convergence performance of different models, which simultaneously verifies the effectiveness and superiority of the proposed model.https://ieeexplore.ieee.org/document/9551969/Time-varying complex-valued Sylvester equation (TVCVSE)zeroing neural network (ZNN)adaptive coefficientnon-convex projection |
| spellingShingle | Jiahao Wu Chengze Jiang Baitao Chen Qixiang Mei Xiuchun Xiao Solving Time-Varying Complex-Valued Sylvester Equation via Adaptive Coefficient and Non-Convex Projection Zeroing Neural Network IEEE Access Time-varying complex-valued Sylvester equation (TVCVSE) zeroing neural network (ZNN) adaptive coefficient non-convex projection |
| title | Solving Time-Varying Complex-Valued Sylvester Equation via Adaptive Coefficient and Non-Convex Projection Zeroing Neural Network |
| title_full | Solving Time-Varying Complex-Valued Sylvester Equation via Adaptive Coefficient and Non-Convex Projection Zeroing Neural Network |
| title_fullStr | Solving Time-Varying Complex-Valued Sylvester Equation via Adaptive Coefficient and Non-Convex Projection Zeroing Neural Network |
| title_full_unstemmed | Solving Time-Varying Complex-Valued Sylvester Equation via Adaptive Coefficient and Non-Convex Projection Zeroing Neural Network |
| title_short | Solving Time-Varying Complex-Valued Sylvester Equation via Adaptive Coefficient and Non-Convex Projection Zeroing Neural Network |
| title_sort | solving time varying complex valued sylvester equation via adaptive coefficient and non convex projection zeroing neural network |
| topic | Time-varying complex-valued Sylvester equation (TVCVSE) zeroing neural network (ZNN) adaptive coefficient non-convex projection |
| url | https://ieeexplore.ieee.org/document/9551969/ |
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