文献类型：期刊文献

英文题名：Modified Newton Integration Neural Algorithm for Dynamic Complex-Valued Matrix Pseudoinversion Applied to Mobile Object Localization

作者：Huang, Haoen[1,2];Fu, Dongyang[1,2];Xiao, Xiuchun[1,2];Ning, Yangyang[1,2];Wang, Huan[1,2];Jin, Long[1,2,3];Liao, Shan[4]

机构：[1]Guangdong Ocean Univ, Sch Elect & Informat Engn, Zhanjiang 524088, Peoples R China;[2]Guangdong Ocean Univ, Shenzhen Inst Guangdong Ocean Univ sity, Shenzhen 518108, Guangdong, Peoples R China;[3]Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Gansu, Peoples R China;[4]Sichuan Univ, Coll Cybersecur, Chengdu 610065, Sichuan, Peoples R China

年份：2021

卷号：17

期号：4

起止页码：2432

外文期刊名：IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

收录：SCI-EXPANDED(收录号：WOS:000607814600014)、、EI(收录号：20210409810813)、Scopus(收录号：2-s2.0-85095946396)、WOS

基金：This work was supported in part by the Fund of Southern Marine Science and Engineering Guangdong Laboratory of Zhanjiang, China under Grant ZJW-2019-08, in part by the Key Projects of the Guangdong Education Department under Grant 2019KZDXM019, in part by the High-Level Marine Discipline Team Project of Guangdong Ocean University under Grant 002026002009, in part by the Guangdong Graduate Academic Forum Project under Grant 230420003, in part by the "First Class" Discipline Construction Platform Project in 2019 of Guangdong Ocean University under Grant 231419026, in part by the Innovation and Strength Project in Guangdong Province, China (Natural Science) under Grant 230419065, in part by the Key Lab of Digital Signal and Image Processing of Guangdong Province, China under Grant 2019GDDSIPL-01, in part by the Industry-University-Research Cooperation Education Project of Ministry of Education under Grant 201801328005, in part by the Guangdong Graduate Education Innovation Project, Graduate Summer School under Grant 2020SQXX19, in part by the Guangdong Graduate Education Innovation Project, Graduate Academic Forum under Grant 2020XSLT27, in part by the Doctoral Initiating Project of Guangdong Ocean University under Grant E13428, and in part by the Special Project in Key Fields of Universities in Department of Education of Guangdong Province, China under Grant 2019033. Paper no. TII-20-0256.

语种：英文

外文关键词：Complex-valued; matrix pseudoinversion; modified Newton integration (MNI) neural algorithm; noise-suppressing ability

外文摘要：A dynamic complex-valued matrix pseudoinversion (DCVMP) is encountered in some special environments, where the system parameters contain the dynamic, magnitude, and phase information. Currently, most of the existing models are employed to the DCVMP under a noise-free workspace. However, the noise perturbation is unavoidable in the practical application scenarios. Therefore, the motivation of this article is to design a computational model for the DCVMP with strong robustness and high-precision computing solutions. To this end, a modified Newton integration (MNI) neural algorithm is proposed for the DCVMP with noise-suppressing ability in this article. Besides, the corresponding convergence proofs on the MNI neural algorithm are provided. Furthermore, the numerical simulations and an application to the estimation of mobile object localization, are demonstrated to illustrate the superiority of the MNI neural algorithm.