Improved finite-time zeroing neural network for time-varying division

被引：12

作者：

Gerontitis, Dimitris ^{[1
]}

Behera, Ratikanta ^{[2
]}

Sahoo, Jajati Keshari ^{[3
]}

Stanimirovic, Predrag S. ^{[4
]}

机构：

[1] Aristotle Univ Thessaloniki, Sch Informat, Thessaloniki, Greece

[2] Univ Cent Florida, Dept Math, Orlando, FL 32826 USA

[3] BITS Pilani KK Birla Goa Campus, Dept Math, Sancoale, Goa, India

[4] Univ Nis, Fac Sci & Math, Nish, Serbia

来源：

STUDIES IN APPLIED MATHEMATICS | 2021年 / 146卷 / 02期

关键词：

complex varying parameter VPFTZNN; division‐ by zero; Euclidean division; finite time ZNN; time‐ varying division; zeroing neural network; DESIGN FORMULA; DYNAMICS; EQUATION; CONVERGENCE; ZFS;

D O I：

10.1111/sapm.12354

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

A novel complex varying-parameter finite-time zeroing neural network (VPFTZNN) for finding a solution to the time-dependent division problem is introduced. A comparative study in relation to the zeroing neural network (ZNN) and finite-time zeroing neural network (FTZNN) is established in terms of the error function and the convergence speed. The error graphs of the VPFTZNN design show promising results and perform better than corresponding ZNN and FTZNN graphs. The proposed dynamical systems are suitable tools for overcoming the division by zero difficulty, which appears in the time-varying division. An application of the introduced VPFTZNN model in an output tracking control time-varying linear system is demonstrated.

引用

页码：526 / 549

页数：24

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