文献类型：期刊文献

英文题名：Modified Newton integration neural algorithm for solving the multi-linear M-tensor equation

作者：Huang, Haoen[1,2];Fu, Dongyang[1,2];Zhang, Jiazheng[3];Xiao, Xiuchun[1,2];Wang, Guancheng[4];Liao, Shan[5]

机构：[1]Guangdong Ocean Univ, Sch Elect & Informat Engn, Zhanjiang 524088, Peoples R China;[2]Guangdong Ocean Univ, Shenzhen Inst, Shenzhen 518108, Peoples R China;[3]Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China;[4]Univ Macau, Fac Sci & Technol, Dept Comp & Informat Sci, Macau 999078, Peoples R China;[5]Sichuan Univ, Coll Cybersecur, Chengdu 610065, Peoples R China

年份：2020

卷号：96

外文期刊名：APPLIED SOFT COMPUTING

收录：SCI-EXPANDED(收录号：WOS:000582762000079)、、WOS

基金：This work is supported in part by the Foud 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.

语种：英文

外文关键词：Multi-linear M-tensor equation; Modified Newton integration (MNI) neural algorithm; Noise-suppression ability

外文摘要：This paper attends to solve the multi-linear equations with special structure, e.g., the multi-linear M-tensor equation, which frequently appears in engineering applications such as deep learning and hypergraph. For its critical and promising role, there are numbers of resolving schemes devoting to obtain a high-performing solution of the multi-linear M-tensor equation. However, few investigations are discovered with noise-suppression ability till now. To be proper with digital devices and further improve the solving effectiveness, it is desirable to design a discrete-time computational algorithm with noise-suppression ability and high-performing property. Inspired by the aforementioned requirements, this paper proposes a modified Newton integration (MNI) neural algorithm for solving the multilinear M-tensor equation with noise-suppression ability. Additionally, the corresponding robustness analyses on the proposed MNI neural algorithm are provided. Simultaneously, computer simulative experiments are generated to explain the capabilities and availabilities of the MNI neural algorithm in noise suppression. As a result, in terms of noise suppression, the proposed MNI neural algorithm is superior to other related algorithms, such as Newton-Raphson iterative (NRI) algorithm (Ding and Wei, 2016), discrete time neural network (DTNN) algorithm (Wang et al., 2019), and sufficient descent nonlinear conjugate gradient (SDNCG) algorithm (Liu et al., 2020). (C) 2020 Elsevier B.V. All rights reserved.