文献类型：期刊文献

英文题名：A parallel computing method based on zeroing neural networks for time-varying complex-valued matrix Moore-Penrose inversion

作者：Xiao, Xiuchun[1,3];Jiang, Chengze[1];Lu, Huiyan[4];Jin, Long[3,4];Liu, Dazhao[1,2,3];Huang, Haoen[1];Pan, Yi[5]

机构：[1]Guangdong Ocean Univ, Sch Elect & Informat Engn, Zhanjiang 524088, Peoples R China;[2]Marine Resources Big Data Ctr South China Sea, Southern Marine Sci & Engn Guangdong Lab, Zhanjiang 524000, Peoples R China;[3]Shenzhen Inst Guangdong Ocean Univ, Shenzhen 518108, Peoples R China;[4]Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China;[5]Georgia State Univ, Dept Comp Sci, Atlanta, GA 30302 USA

年份：2020

卷号：524

起止页码：216

外文期刊名：INFORMATION SCIENCES

收录：SCI-EXPANDED(收录号：WOS:000530095300014)、、EI(收录号：20201508401894)、Scopus(收录号：2-s2.0-85082853339)、WOS

基金：This work is supported by the Fund of Southern Marine Science and Engineering Guangdong Laboratory of Zhanjiang, China (with no. ZJW-2019-08), by the Innovation and Strength Project in Guangdong Province, China (Natural Science) (with no. 230419065), by the Key Lab of Digital Signal and Image Processing of Guangdong Province, China (with no. 2019GDDSIPL-01), by the Industry-University-Research Cooperation Education Project of Ministry of Education (with no. 201801328005), by the Guangdong Graduate Education Innovation Project, Graduate Summer School (with no. 2020SQXX19), by the Guangdong Graduate Education Innovation Project, Graduate Academic Forum (with no. 2020XSLT27), by the Doctoral Initiating Project of Guangdong Ocean University (with no. E13428), and also by the Special Project in Key Fields of Universities in Department of Education of Guangdong Province, China (with no. 2019033).

语种：英文

外文关键词：Zeroing neural network; Parallel computing method; Dynamic matrix Moore-Penrose inverse; Complex domain

外文摘要：This paper analyzes the existing zeroing neural network (ZNN) models from the perspective of control theory. It proposes an exclusive ZNN model for solving the dynamic complex-valued matrix Moore-Penrose inverse problem: the complex-valued zeroing neural network (CVZNN). Then, a method of constructing a special type of saturation-allowed activation function is defined, which relaxes the convex constraint on the activation function when constructing the ZNN model. The convergence of the CVZNN model activated by proposed saturation-allowed functions is analyzed. Besides, the robustness of the CVZNN model under different types of noise interference is investigated based on the perspective of the control theory. Finally, the effectiveness and superiority of the CVZNN model are illustrated by simulation experiments. (C) 2020 Elsevier Inc. All rights reserved.