文献类型：期刊文献

英文题名：Improved ZND model for solving dynamic linear complex matrix equation and its application

作者：Song, Zhiyuan[1];Lu, Zhenyao[1];Wu, Jiahao[1];Xiao, Xiuchun[1];Wang, Guancheng[1]

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

年份：2022

卷号：34

期号：23

起止页码：21035

外文期刊名：NEURAL COMPUTING & APPLICATIONS

收录：SCI-EXPANDED(收录号：WOS:000829712700007)、、EI(收录号：20223012412886)、Scopus(收录号：2-s2.0-85134766102)、WOS

基金：This work is supported in part by the Natural Science Foundation of Guangdong Province, China (No. 2021A1515011847), in part by Postgraduate Education Innovation Project of Guangdong Ocean University (No. 202214, 202250, 202251, 202160), in part by the Special Project in Key Fields of Universities in Department of Education of Guangdong Province (No. 2019KZDZX1036), in part by the Key Lab of Digital Signal and Image Processing of Guangdong Province (No. 2019GDDSIPL-01).

语种：英文

外文关键词：Dynamic problems; Linear complex matrix equation; Zeroing neural dynamics (ZND); Adaptive coefficient; Non-convex activation function

外文摘要：The online solving of a dynamic linear complex matrix equation (DLCME) is commonly encountered in many fields, and it exists for lots of engineering applications. For solving the DLCME, the gradient neural dynamics (GND) and the zeroing neural dynamics (ZND) are usually designed based on a predefined error function in scalar or matrix form to monitor the residual error convergence, respectively. In this paper, in order to improve the adaptability as the residual error decreasing with time and to release the limitation of convex activation function, an improved zeroing neural dynamics (IZND) model is proposed with a residual-based adaptive coefficient and a non-convex activation function. By using the Lyapunov stability theory, the global convergence and noise suppression of the proposed IZND model are theoretically discussed under the noise-free and noise-perturbed circumstances. Then, simulative verifications and a dynamic acoustic source localization application illustrate the efficacy and superiority of the proposed model for solving the DLCME. Comparing with some existing GND and ZND models, the proposed IZND model shows advanced performance in solution accuracy, noise suppression and convergence rate.