Design and Analysis of New Zeroing Neural Network Models With Improved Finite-Time Convergence for Time-Varying Reciprocal of Complex Matrix

被引:18
|
作者
Jian, Zhen [1 ]
Xiao, Lin [2 ]
Dai, Jianhua [2 ]
Tang, Zhuo [1 ]
Liu, Chubo [1 ]
机构
[1] Hunan Univ, Coll Informat Sci & Engn, Changsha 410082, Hunan, Peoples R China
[2] Hunan Normal Univ, Hunan Prov Key Lab Intelligent Comp & Language In, Changsha 410081, Peoples R China
基金
中国国家自然科学基金;
关键词
Convergence; Mathematical model; Real-time systems; Informatics; Upper bound; Recurrent neural networks; Complex matrix inversion; finite-time convergence; upper bound; zeroing neural network (ZNN); SYLVESTER EQUATION; INVERSION;
D O I
10.1109/TII.2019.2941750
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, two improved finite-time convergent complex-valued zeroing neural network (IFTCVZNN) models are presented and investigated for real-time solution of time-varying reciprocal of complex matrices on account of two equivalent processing ways of complex calculations for nonlinear activation functions. Furthermore, a novel nonlinear activation function is explored to modify the comprehensive performance of such two IFTCVZNN models. Compared with existing complex-valued neural networks converging within the limited time, the proposed IFTCVZNN models with the new activation function have better finite-time convergence and less conservative upper bound. Numerical simulations verify that the maximum of convergence time estimated via Lyapunov stability is theoretically much closer to the actual convergence time.
引用
收藏
页码:3838 / 3848
页数:11
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