文献类型：期刊文献

英文题名：Solving Perturbed Time-Varying Linear Equation and Inequality Problem With Adaptive Enhanced and Noise Suppressing Zeroing Neural Network

作者：Wu, Chaomin[1];Huang, Zifan[1];Wu, Jiahao[1];Lin, Cong[1]

机构：[1]Guangdong Ocean Univ, Sch Elect & Informat Engn, Zhanjiang 524088, Peoples R China

年份：2022

卷号：10

起止页码：77396

外文期刊名：IEEE ACCESS

收录：SCI-EXPANDED(收录号：WOS:000832939900001)、、EI(收录号：20222812348240)、Scopus(收录号：2-s2.0-85133658350)、WOS

基金：This work was supported by the Guangdong Basic and Applied Basic Research Foundation under Grant 2021A1515011847.

语种：英文

外文关键词：Mathematical models; Adaptation models; Convergence; Time-varying systems; Numerical models; Heuristic algorithms; Noise measurement; Time-varying linear equation and inequality; noises perturbance; zeroing neural network (ZNN); adaption factor; dynamic positioning

外文摘要：Solving time-varying linear equation and inequality (TVLEI) problem has attracted extensive attention in numerous scientific and engineered fields. In this article, it is basically considered that the commonly used dynamics neural network in the virtual environment is inevitably interfered with by the variable measurement noises while dealing with the TVLEI problem. An adaptive enhanced and noise-suppressing zeroing neural network (AENSZNN) model is proposed as an improved algorithm for solving the TVLEI problem. An adaptive scale factor based on the residual error norm is designed to make the proposed AENSZNN model converge to the theoretical solution faster. Furthermore, the momentum enhancement terms added to the model enables the AENSZNN model to effectively solve the TVLEI problem in real-time under the obstruction of different measurement noises. Besides, theoretical results and numerical experiments indicate that the AENSZNN model has advantages in convergence accuracy and robustness to noises compared with the existing algorithms. Note that, the proposed AENSZNN model is successfully exploited for the estimation of mobile object localization.