文献类型：期刊文献

英文题名：RNN for Solving Time-Variant Generalized Sylvester Equation With Applications to Robots and Acoustic Source Localization

作者：Jin, Long[1];Yan, Jingkun[1];Du, Xiujuan[2,3,4];Xiao, Xiuchun[5,6];Fu, Dongyang[5,6]

机构：[1]Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China;[2]Qinghai Normal Univ, Dept Comp, Xining 810008, Peoples R China;[3]Key Lab Internet Things Qinghai Prov, Xining 810008, Peoples R China;[4]Acad Plateau Sci & Sustainabil, Xining 810008, Peoples R China;[5]Guangdong Ocean Univ, Sch Elect & Informat Engn, Zhanjiang 524088, Peoples R China;[6]Guangdong Ocean Univ, Shenzhen Inst, Shenzhen 518108, Peoples R China

年份：2020

卷号：16

期号：10

起止页码：6359

外文期刊名：IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

收录：SCI-EXPANDED(收录号：WOS:000545243500014)、、EI(收录号：20202908940774)、Scopus(收录号：2-s2.0-85080862263)、WOS

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 61703189, in part by the National Key Research and Development Program of China under Grant 2017YFE0118900, in part by the Natural Science Foundation of Gansu Province, China, under Grant 18JR3RA264, in part by the Sichuan Science and Technology Program under Grant 19YYJC1656, in part by the Fundamental Research Funds for the Central Universities under Grant lzujbky-2019-89, in part by the Innovation and Strength Project in Guangdong Province (Natural Science) under Grant 230419065, and in part by the Industry-University-Research Cooperation Education Project of Ministry of Education under Grant 201801328005. Paper no. TII-19-3219.

语种：英文

外文关键词：Mathematical model; Computational modeling; Acoustics; Convergence; Informatics; Recurrent neural networks; Task analysis; Acoustic source localization; mobile manipulator; recurrent neural network (RNN); time-variant generalized Sylvester equation (TVGSE)

外文摘要：A generalized Sylvester equation is a special formulation containing the Sylvester equation, the Lyapunov equation and the Stein equation, which is often encountered in various fields. However, the time-variant generalized Sylvester equation (TVGSE) is rarely investigated in the existing literature. In this article, we propose a noise-suppressing recurrent neural network (NSRNN) model activated by saturation-allowed functions to solve the TVGSE. For comparison, the existing zeroing neural network (ZNN) models and some improved ZNN models are introduced. Additionally, theoretical analysis on the convergence and robustness of the NSRNN model is given. Furthermore, computer simulations on illustrative examples and applications to robots and acoustic source localization are carried out. Validation results synthesized by the NSRNN model and other ZNN models are provided to illustrate the ability in solving the TVGSE and dealing with noises of the NSRNN model, and the inaction of other ZNN models to noises.