文献类型：期刊文献

英文题名：Noise-tolerant gradient-oriented neurodynamic model for solving the Sylvester equation

作者：Liu, Bei[1];Fu, Dongyang[1];Qi, Yimeng[2,3];Huang, Haoen[1];Jin, Long[2,3]

机构：[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

年份：2021

卷号：109

外文期刊名：APPLIED SOFT COMPUTING

收录：SCI-EXPANDED(收录号：WOS:000685652800013)、、EI(收录号：20212410507888)、Scopus(收录号：2-s2.0-85107780289)、WOS

基金：This work was supported by Key projects of the Guangdong Education Department under Grant 2019KZDXM019; Fund of Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang) under Grant ZJW201908; High-level marine disci-pline team project of Guangdong Ocean University under Grant 002026002009; Guangdong graduate academic forum project under Grant 230420003; The "first class"discipline construction platform project in 2019 of Guangdong Ocean University un-der Grant 231419026; Natural Science Foundation of Chongqing (China) under Grant cstc2020jcyjzdxmX0028; in part by CAS "Light of West China"Program.

语种：英文

外文关键词：Gradient neural network (GNN); Noise-tolerant gradient-oriented; neurodynamics (NTGON); Sylvester equation; Dynamic-system approach

外文摘要：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.