A family of varying-parameter finite-time zeroing neural networks for solving time-varying Sylvester equation and its application

被引：27
|
作者：
Gerontitis, Dimitrios [1 ]
Behera, Ratikanta [2 ]
Tzekis, Panagiotis [1 ]
Stanimirovic, Predrag [3 ]
机构：
[1] Int Hellen Univ, Dept Informat & Elect Engn, Thessaloniki, Greece
[2] Univ Cent Florida, Dept Math, Orlando, FL 32826 USA
[3] Univ Nis, Fac Sci & Math, Visegradska 33, Nish 18000, Serbia
来源：
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS | 2022年 / 403卷
关键词：
Recurrent neural network; Sylvester equation; Zeroing neural network (ZNN); Varying-parameter finite-time zeroing neural network (VPFTZNN); ITERATIVE ALGORITHM; DESIGN FORMULA; CONVERGENCE;
D O I：
10.1016/j.cam.2021.113826
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
A family of varying-parameter finite-time zeroing neural networks (VPFTZNN) is introduced for solving the time-varying Sylvester equation (TVSE). The convergence speed of the proposed VPFTZNN family is analysed and compared with the traditional zeroing neural network (ZNN) and the finite-time zeroing neural network (FTZNN). The behaviour of the proposed neural networks under various activation functions is proved theoretically and verified experimentally. In addition, the stability and noise resistance of the proposed VPFTZNN family are discussed. Further, the proposed VPFTZNN models are applied in the computation of current flows in an electrical network. (c) 2021 Elsevier B.V. All rights reserved.
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页数：20
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