文献类型：期刊文献

英文题名：Residual Error Feedback Zeroing Neural Network for Solving Time-Varying Sylvester Equation

作者：Li, Kunjian[1];Jiang, Chengze[1];Xiao, Xiuchun[1];Huang, Haoen[1];Li, Yongjiang[2];Yan, Jingwen[3]

机构：[1]Guangdong Ocean Univ, Sch Elect & Informat Engn, Zhanjiang 524088, Peoples R China;[2]Guangdong Ocean Univ, Sch Math & Comp, Zhanjiang 524088, Peoples R China;[3]Shantou Univ, Coll Engn, Shantou 515063, Peoples R China

年份：2022

卷号：10

起止页码：2860

外文期刊名：IEEE ACCESS

收录：SCI-EXPANDED(收录号：WOS:000741986400001)、、EI(收录号：20220111426009)、Scopus(收录号：2-s2.0-85122091596)、WOS

基金：This work was supported in part by the Guangdong Basic and Applied Basic Research Foundation under grant 2021A1515011847, in part by the Special Project in Key Fields of Universities in Department of Education of Guangdong Province under grant 2019KZDZX1036, in part by the Guangdong Graduate Education Innovation Project, Graduate Summer School under grant 2020SQXX19, in part by the Guangdong Graduate Education Innovation Project, Graduate Academic Forum under Grant 202101, in part by the Key Lab of Digital Signal and Image Processing of Guangdong Province under Grant 2019GDDSIPL-01, in part by the Postgraduate Education Innovation Project of Guangdong Ocean University under Grant 202160, in part by the Innovative and Strong School Team Building Project under Grant 2017KCXTD015.

语种：英文

外文关键词：Mathematical models; Computational modeling; Adaptation models; Convergence; Analytical models; Education; Adaptive systems; Time-varying problems; residual error; feedback; zeroing neural network (ZNN); Sylvester equation

外文摘要：In many fields, the issue of solving the time-varying Sylvester equation (TVSE) is commonly encountered. Consequently, finding its exact solution has become a research hotspot. In general, the ZNN and IEZNN models are the most useful algorithms that are frequently utilized to solve the TVSE problem. However, the ZNN model is borned with noise susceptibility and the IEZNN model loses the adaptive performance due to its constant coefficient in solving the TVSE problem. In this paper, a residual error feedback zeroing neural network (REFZNN) is proposed to adaptively solve the TVSE problem. The REFZNN model feeds back the residual error to the solustion system, which forms a feedback regulation to reduce the residual error between the system output and the system target. Then, the convergence and noise patience of the REFZNN model are proved by theoretical analyses. Finally, the validity of the proposed model is verified by designing computer simulation experiments and its superiority is confirmed by the performance comparisons with the ZNN and IEZNN models.