文献类型：期刊文献

英文题名：Modified Newton Integration Neural Algorithm for Solving Time-Varying Yang-Baxter-Like Matrix Equation

作者：Huang, Haoen[1];Huang, Zifan[1];Wu, Chaomin[1];Jiang, Chengze[1];Fu, Dongyang[1];Lin, Cong[1]

机构：[1]Guangdong Ocean Univ GDOU, Sch Elect & Informat Engn, Zhanjiang 524025, Peoples R China

年份：2023

卷号：55

期号：1

起止页码：773

外文期刊名：NEURAL PROCESSING LETTERS

收录：SCI-EXPANDED(收录号：WOS:000850756900002)、、EI(收录号：20223712720985)、Scopus(收录号：2-s2.0-85137533547)、WOS

基金：This work was supported by Key Projects of the Guangdong Education Department (2019KZDXM019); Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang) (ZJW-2019-08); High-Level Marine Discipline Team Project of Guangdong Ocean University (00202600-2009).

语种：英文

外文关键词：Time-varying Yang-Baxter-like matrix equation (TVYBLME ); Modified Newton integration (MNI ) neural algorithm; Noise-tolerance ability

外文摘要：This paper intends to solve the time-varying Yang-Baxter-like matrix equation (TVYBLME), which is frequently employed in the fields of scientific computing and engineering applications. Due to its critical and promising role, several methods have been constructed to generate a high-performing solution for the TVYBLME. However, given the fact that noise is ubiquitous and inevitable in actual systems. It is necessary to design a computational algorithm with strong robustness to solve the TVYBLME, which has rarely been mentioned previously. For this reason, to remedy shortcomings that the conventional computing methods have encountered in a noisy case, a modified Newton integration (MNI) neural algorithm is proposed and employed to solve the TVYBLME. In addition, the related theoretical analyses show that the proposed MNI neural algorithm has the noise-tolerance ability under various noisy cases. Finally, the feasibility and superiority of the proposed MNI neural algorithm to solve the TVYBLME are verified by simulation experiments.