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Neural Processing Letters
Erschienen in: Neural Processing Letters 3/2015

01.12.2015

Finite-Time Stability and Its Application for Solving Time-Varying Sylvester Equation by Recurrent Neural Network

verfasst von: Yanjun Shen, Peng Miao, Yuehua Huang, Yi Shen

Erschienen in: Neural Processing Letters | Ausgabe 3/2015

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Abstract

This paper investigates finite-time stability and its application for solving time-varying Sylvester equation by recurrent neural network. Firstly, a new finite-time stability criterion is given and a less conservative upper bound of the convergence time is also derived. Secondly, a sign-bi-power activation function with a linear term is presented for the recurrent neural network. The estimation of the upper bound of the convergence time is more less conservative. Thirdly, it is proposed a tunable activation function with three tunable positive parameters for the recurrent neural network. These parameters are not only helpful to reduce conservatism of the upper bound of the convergence time, accelerate convergence but also reduce sensitivity to additive noise. The effectiveness of our methods is shown by both theoretical analysis and numerical simulations.
Vorheriger Artikel A Novel Multiple Kernel Learning Method Based on the Kullback–Leibler Divergence

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Metadaten
Titel
Finite-Time Stability and Its Application for Solving Time-Varying Sylvester Equation by Recurrent Neural Network
verfasst von
Yanjun Shen
Peng Miao
Yuehua Huang
Yi Shen
Publikationsdatum
01.12.2015
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 3/2015
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-014-9397-y

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