Noise-tolerant gradient-oriented neurodynamic model for solving the Sylvester equation
文献类型：期刊论文
| 作者 | Liu, Bei1; Fu, Dongyang1; Qi, Yimeng2,3; Huang, Haoen1; Jin, Long2,3 |
| 刊名 | APPLIED SOFT COMPUTING
 |
| 出版日期 | 2021-09-01 |
| 卷号 | 109页码:12 |
| 关键词 | Gradient neural network (GNN) Noise-tolerant gradient-oriented neurodynamics (NTGON) Sylvester equation Dynamic-system approach |
| ISSN号 | 1568-4946 |
| DOI | 10.1016/j.asoc.2021.107514 |
| 通讯作者 | Fu, Dongyang(fdy163@163.com) ; Jin, Long(jinlongsysu@foxmail.com) |
| 英文摘要 | Recursive neural networks are generally divided into dynamic neural networks and static neural networks to refer to the neural networks with one or more feedback links in the network structure. Inevitably, there exist some problems such as poor approximation performance and poor stable convergence performance due to complex network structure. The noise-tolerant gradient-oriented neurodynamic (NTGON) model proposed in this study is an improved model based on the traditional idea of a gradient neural network (GNN) model. The proposed NTGON model can obtain accurate and efficient results under the condition of various noises when computing the Sylvester equation, which is effectively used to solve various problems with noise pollution that are frequently encountered in practical engineering. Compared with the original GNN model for the Sylvester equation, the NTGON model exponentially converges to the theoretical solution starting from any initial state. It is demonstrated that the noise-polluted NTGON model converges to the theoretical solution globally no matter how large the unknown matrix-form noise is. Furthermore, simulation results show that the proposed NTGON model achieves a performance that is superior to that of the original GNN model for solving the Sylvester equation in the presence of noise. (C) 2021 Elsevier B.V. All rights reserved. |
| 资助项目 | Key projects of the Guangdong Education Department[2019KZDXM019] ; Fund of Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang)[ZJW-2019-08] ; Guangdong graduate academic forum project[230420003] ; first classdiscipline construction platform project in 2019 of Guangdong Ocean University[231419026] ; Natural Science Foundation of Chongqing (China)[cstc2020jcyjzdxmX0028] ; CAS Light of West China Program ; High-level marine disci-pline team project of Guangdong Ocean University[002026002009] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000685652800013 |
| 出版者 | ELSEVIER |
| 源URL | [http://119.78.100.138/handle/2HOD01W0/13903]  |
| 专题 | 中国科学院重庆绿色智能技术研究院 |
| 通讯作者 | Fu, Dongyang; Jin, Long |
| 作者单位 | 1.Guangdong Ocean Univ, Coll Elect & Informat Engn, Zhanjiang 524088, Peoples R China 2.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing 400714, Peoples R China 3.Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Bei,Fu, Dongyang,Qi, Yimeng,et al. Noise-tolerant gradient-oriented neurodynamic model for solving the Sylvester equation[J]. APPLIED SOFT COMPUTING,2021,109:12. |
| APA | Liu, Bei,Fu, Dongyang,Qi, Yimeng,Huang, Haoen,&Jin, Long.(2021).Noise-tolerant gradient-oriented neurodynamic model for solving the Sylvester equation.APPLIED SOFT COMPUTING,109,12. |
| MLA | Liu, Bei,et al."Noise-tolerant gradient-oriented neurodynamic model for solving the Sylvester equation".APPLIED SOFT COMPUTING 109(2021):12. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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