Finite-Time Convergence and Robustness Analysis of Two Nonlinear Activated ZNN Models for Time-Varying Linear Matrix Equations

被引:19
作者
Xiao, Lin [1 ]
Jia, Lei [1 ]
Zhang, Yongsheng [2 ]
Hu, Zeshan [3 ]
Dai, Jianhua [1 ]
机构
[1] Hunan Normal Univ, Coll Informat Sci & Engn, Changsha 410081, Peoples R China
[2] Jishou Univ, Coll Informat Sci & Engn, Jishou 416000, Peoples R China
[3] Hunan Univ, Coll Informat Sci & Engn, Changsha 410082, Peoples R China
基金
中国国家自然科学基金;
关键词
Time-varying linear matrix equation; zeroing neural network (ZNN); activation functions; finite-time convergence; steady state residual error; RECURRENT NEURAL-NETWORK; DESIGN;
D O I
10.1109/ACCESS.2019.2941961
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Based on zeroing neural network (ZNN), this paper designs two nonlinear activated ZNN (NAZNN) models for time-varying linear matrix equation through taking two new activation functions into consideration. The purpose of constructing the novel models is to solve the problem of time-varying linear matrix equation quickly and precisely. Theoretical analysis proves that two new activation functions can not only accelerate the convergence rate of the prime ZNN models but also come true finite-time convergence. After adding differential error and model-implementation error into the models, the theoretical upper bounds of the steady state residual errors are calculated, which demonstrate the superior robustness of the proposed two NAZNN models. Finally, comparative simulation results show the excellent performance of the proposed two NAZNN models by solving time-varying linear matrix equation.
引用
收藏
页码:135133 / 135144
页数:12
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