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

Jie Jin, Lin Xiao, Ming Lu, Jichun Li

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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 languageEnglish
Article number8691448
Pages (from-to)58945-58950
Number of pages6
JournalIEEE Access
Volume7
DOIs
Publication statusPublished - 15 Apr 2019

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Recurrent neural networks
Neural networks
Computer simulation

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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",
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Design and Analysis of Two FTRNN Models with Application to Time-Varying Sylvester Equation. / Jin, Jie; Xiao, Lin; Lu, Ming; Li, Jichun.

In: IEEE Access, Vol. 7, 8691448, 15.04.2019, p. 58945-58950.

Research output: Contribution to journalArticleResearchpeer-review

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