Zeroing Neural Network for Time-Varying Linear Equations With Application to Dynamic Positioning

被引:36
作者
Dai, Jianhua [1 ,2 ,3 ]
Li, Yiwei [1 ,2 ,3 ]
Xiao, Lin [1 ,2 ,3 ]
Jia, Lei [1 ,2 ,3 ]
机构
[1] Hunan Normal Univ, Hunan Prov Key Lab Intelligent Comp & Language In, Changsha 410081, Hunan, Peoples R China
[2] Hunan Normal Univ, MOE LCSM, Changsha 410081, Hunan, Peoples R China
[3] Hunan Xiangjiang Artificial Intelligence Acad, Changsha 410081, Hunan, Peoples R China
基金
中国国家自然科学基金;
关键词
Mathematical model; Convergence; Analytical models; Numerical models; Heuristic algorithms; Neural networks; Informatics; Dynamic positioning; predefined-time convergence; time-varying linear equations; zeroing neural network (ZNN); FINITE-TIME; SYLVESTER EQUATION; DESIGN; CONVERGENCE; SYSTEM;
D O I
10.1109/TII.2021.3087202
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this article, considering the effectiveness and efficiency in solving time-varying problems, a new zeroing neural network (ZNN) is proposed to solve time-varying linear equations with column full rank coefficient matrix. In addition, two novel nonlinear activation functions are developed to enhance the comprehensive performance of the ZNN model. It is demonstrated through theoretical analysis and numerical experiments that the nonlinear activated ZNN model has better noise immunity, and faster prescribed-time convergence speed. Finally, the ZNN method is successfully applied to 2-D and 3-D dynamic positioning, with lower positioning error than the traditional pseudoinverse method.
引用
收藏
页码:1552 / 1561
页数:10
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