Design and Analysis of Two FTRNN Models with Application to Time-Varying Sylvester Equation

Jie Jin; Lin Xiao; Ming Lu; Jichun Li

doi:10.1109/ACCESS.2019.2911130

Design and Analysis of Two FTRNN Models with Application to Time-Varying Sylvester Equation

Jie Jin, Lin Xiao, Ming Lu, Jichun Li

SCEDT Engineering

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Abstract

In this paper, to accelerate the convergence speed of Zhang neural network (ZNN), two finite-time recurrent neural networks (FTRNNs) are presented via devising two novel design formulas. For verifying the advantages of the proposed FTRNN models, a solution application to time-varying Sylvester equation (TVSE) is given. Compared with the conventional ZNN model, the presented new FTRNN models in this paper are theoretically proved to have better convergence performance, and they are more effective for online solving TVSE within finite time. At last, the superiority and effectiveness of the new FTRNN models for solving TVSE are verified by numerical simulations.

Original language	English
Article number	8691448
Pages (from-to)	58945-58950
Number of pages	6
Journal	IEEE Access
Volume	7
DOIs	https://doi.org/10.1109/ACCESS.2019.2911130
Publication status	Published - 15 Apr 2019

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10.1109/ACCESS.2019.2911130

Design and Analysis of Two FTRNN ModelsAccepted author manuscript, 348 KBLicence: CC BY-NC-ND

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title = "Design and Analysis of Two FTRNN Models with Application to Time-Varying Sylvester Equation",

abstract = "In this paper, to accelerate the convergence speed of Zhang neural network (ZNN), two finite-time recurrent neural networks (FTRNNs) are presented via devising two novel design formulas. For verifying the advantages of the proposed FTRNN models, a solution application to time-varying Sylvester equation (TVSE) is given. Compared with the conventional ZNN model, the presented new FTRNN models in this paper are theoretically proved to have better convergence performance, and they are more effective for online solving TVSE within finite time. At last, the superiority and effectiveness of the new FTRNN models for solving TVSE are verified by numerical simulations.",

author = "Jie Jin and Lin Xiao and Ming Lu and Jichun Li",

year = "2019",

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doi = "10.1109/ACCESS.2019.2911130",

language = "English",

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AU - Jin, Jie

AU - Xiao, Lin

AU - Lu, Ming

AU - Li, Jichun

PY - 2019/4/15

Y1 - 2019/4/15

N2 - In this paper, to accelerate the convergence speed of Zhang neural network (ZNN), two finite-time recurrent neural networks (FTRNNs) are presented via devising two novel design formulas. For verifying the advantages of the proposed FTRNN models, a solution application to time-varying Sylvester equation (TVSE) is given. Compared with the conventional ZNN model, the presented new FTRNN models in this paper are theoretically proved to have better convergence performance, and they are more effective for online solving TVSE within finite time. At last, the superiority and effectiveness of the new FTRNN models for solving TVSE are verified by numerical simulations.

AB - In this paper, to accelerate the convergence speed of Zhang neural network (ZNN), two finite-time recurrent neural networks (FTRNNs) are presented via devising two novel design formulas. For verifying the advantages of the proposed FTRNN models, a solution application to time-varying Sylvester equation (TVSE) is given. Compared with the conventional ZNN model, the presented new FTRNN models in this paper are theoretically proved to have better convergence performance, and they are more effective for online solving TVSE within finite time. At last, the superiority and effectiveness of the new FTRNN models for solving TVSE are verified by numerical simulations.

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JO - IEEE Access

JF - IEEE Access

M1 - 8691448

ER -

Design and Analysis of Two FTRNN Models with Application to Time-Varying Sylvester Equation

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