Noise-tolerate and adaptive coefficient zeroing neural network for solving dynamic matrix square root  

作　　者：Xiuchun Xiao Chengze Jiang Qixiang Mei Yudong Zhang 

机构地区：[1]School of Electronics and Information Engineering,Guangdong Ocean University,Zhanjiang,China [2]School of Cyber Science and Engineering,Southeast University,Nanjing,China [3]School of Computing and Mathematical Sciences,University of Leicester,Leicester,UK

出　　处：《CAAI Transactions on Intelligence Technology》2024年第1期167-177,共11页智能技术学报（英文）

基　　金：Natural Science Foundation of Guangdong Province,Grant/Award Number:2021A1515011847;Special Project in Key Fields of Universities in Department of Education of Guangdong Province,Grant/Award Number:2019KZDZX1036;Demonstration Bases for Joint Training of Postgraduates of Department of Education of Guangdong Province,Grant/Award Number:202205;Key Lab of Digital Signal and Image Processing of Guangdong Province,Grant/Award Number:2019GDDSIPL-01;Innovation and Entrepreneurship Training Program for College Students of Guangdong Ocean University,Grant/Award Number:202210566028;Postgraduate Education Innovation Plan Project of Guangdong Ocean University,Grant/Award Numbers:202214,202250,202251,202160。

摘　　要：The solving of dynamic matrix square root(DMSR)problems is frequently encountered in many scientific and engineering fields.Although the original zeroing neural network is powerful for solving the DMSR,it cannot vanish the influence of the noise perturbations,and its constant-coefficient design scheme cannot accelerate the convergence speed.Therefore,a noise-tolerate and adaptive coefficient zeroing neural network(NTACZNN)is raised to enhance the robust noise immunity performance and accelerate the conver-gence speed simultaneously.Then,the global convergence and robustness of the pro-posed NTACZNN are theoretically analysed under an ideal environment and noise-perturbed circumstances.Furthermore,some illustrative simulation examples are designed and performed in order to substantiate the efficacy and advantage of the NTACZNN for the DMSR problem solution.Compared with some existing ZNNs,the proposed NTACZNN possesses advanced performance in terms of noise tolerance,solution accuracy,and convergence rate.

关 键 词：adaptive intelligent systems neural network real-time systems 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]