Double integral-enhanced Zeroing neural network with linear noise rejection for time-varying matrix inverse  

作　　者：Bolin Liao Luyang Han Xinwei Cao Shuai Li Jianfeng Li 

机构地区：[1]College of Computer Science and Engineering,Jishou University,Jishou,China [2]School of Management,Shanghai University,Shanghai,China [3]School of Engineering,Swansea University,Swansea,UK

出　　处：《CAAI Transactions on Intelligence Technology》2024年第1期197-210,共14页智能技术学报（英文）

基　　金：National Natural Science Foundation of China,Grant/Award Numbers:61962023,62066015。

摘　　要：In engineering fields,time-varying matrix inversion(TVMI)issue is often encountered.Zeroing neural network(ZNN)has been extensively employed to resolve the TVMI problem.Nevertheless,the original ZNN(OZNN)and the integral-enhanced ZNN(IEZNN)usually fail to deal with the TVMI problem under unbounded noises,such as linear noises.Therefore,a neural network model that can handle the TVMI under linear noise interference is urgently needed.This paper develops a double integral-enhanced ZNN(DIEZNN)model based on a novel integral-type design formula with inherent linear-noise tolerance.Moreover,its convergence and robustness are verified by deriva-tion strictly.For comparison and verification,the OZNN and the IEZNN models are adopted to resolve the TVMI under multiple identical noise environments.The experi-ments proved that the DIEZNN model has excellent advantages in solving TVMI problems under linear noises.In general,the DIEZNN model is an innovative work and is proposed for the first time.Satisfyingly,the errors of DIEZNN are always less than 1�10−3 under linear noises,whereas the error norms of OZNN and IEZNN models are not convergent to zero.In addition,these models are applied to the control of the controllable permanent magnet synchronous motor chaotic system to indicate the superiority of the DIEZNN.

关 键 词：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]