文献类型：期刊文献

英文题名：Modified gradient neural networks for solving the time-varying Sylvester equation with adaptive coefficients and elimination of matrix inversion

作者：Liao, Shan[1];Liu, Jiayong[1];Xiao, Xiuchun[2,3];Fu, Dongyang[2,3];Wang, Guancheng[2];Jin, Long[2,3]

机构：[1]Sichuan Univ, Coll Cybersecur, Chengdu 610065, Peoples R China;[2]Guangdong Ocean Univ, Shenzhen Inst, Shenzhen 518108, Peoples R China;[3]Guangdong Ocean Univ, Coll Elect & Informat Engn, Zhanjiang 524088, Peoples R China

年份：2020

卷号：379

起止页码：1

外文期刊名：NEUROCOMPUTING

收录：SCI-EXPANDED(收录号：WOS:000507464700001)、、EI(收录号：20194807748921)、Scopus(收录号：2-s2.0-85075534709)、WOS

基金：This work was supported in part by the National Key R&D Program of China under Grant 2017YFE0118900, in part by the National Natural Science Foundation of China under Grant 41340049, 41430968, and 11974084, in part by the Fund of Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang) under Grant ZJW-2019-08, in part by the Science and Technology Planning Project of Guangdong under Grant 2013B030200002 and 2016A020222016, and in part by the Project of Enhancing School with Innovation of Guangdong Ocean University under Grant GDOU2014050226.

语种：英文

外文关键词：Time-varying sylvester equation; Modified gradient-based recurrent neural network (MGRNN); Adaptive coefficients; Elimination of the matrix inversion

外文摘要：In scientific and engineering fields, the solutions to many problems can be transformed into finding the solutions to Sylvester equation, for which various computational methods (e.g., recurrent neural network, RNN) have been presented and investigated. RNN models are frequently used to solve computational problems due to the prevalent exploitation of the gradient-based RNN. However, the overlong convergent time and the too large residual error restrict the widespread applications of the RNN model in solving time-varying problems. Further, a special type of RNN named zeroing neural network (ZNN) is able to solve the time-varying Sylvester equation, which breaks the limitations mentioned above, but fails to handle complex time-varying problems owing to the sharp increment of the calculated amount in matrix inversion involved. To remedy the limitation, a modified gradient-based RNN (MGRNN) model is proposed to generate more accurate computational solutions with less convergent time and adaptive coefficients for solving the time-varying Sylvester equation, which replaces the matrix inversion problem with the matrix transposition problem. Besides, theoretical analyses and mathematical verifications are presented to validate the efficiency and superiority of the proposed MGRNN model compared with the traditional gradient-based recurrent neural network (GRNN) and ZNN models. Furthermore, simulation experiments are conducted to substantiate the properties of the newly proposed MGRNN model for solving the time-varying Sylvester equation. (C) 2019 Elsevier B.V. All rights reserved.