Stability analysis of genetic regulatory networks with switching parameters and time delays

Tingting Yu; Jianxing Liu; Yi Zeng; Xian Zhang; Qingshuang Zeng; Ligang Wu

doi:10.1109/TNNLS.2016.2636185

Stability analysis of genetic regulatory networks with switching parameters and time delays

Tingting Yu
, Jianxing Liu^*
, Yi Zeng
, Xian Zhang
, Qingshuang Zeng
, Ligang Wu

^*Corresponding author for this work

School of Astronautics

Harbin Institute of Technology
Heilongjiang University

Research output: Contribution to journal › Article › peer-review

56 Scopus citations

Abstract

This paper is concerned with the exponential stability analysis of genetic regulatory networks (GRNs) with switching parameters and time delays. In this paper, a new integral inequality and an improved reciprocally convex combination inequality are considered. By using the average dwell time approach together with a novel Lyapunov-Krasovskii functional, we derived some conditions to ensure the switched GRNs with switching parameters and time delays are exponentially stable. Finally, we give two numerical examples to clarify that our derived results are effective.

Original language	English
Pages (from-to)	3047-3058
Number of pages	12
Journal	IEEE Transactions on Neural Networks and Learning Systems
Volume	29
Issue number	7
DOIs	https://doi.org/10.1109/TNNLS.2016.2636185
State	Published - Jul 2018

Keywords

Average dwell time
delay partitioning
exponential stability
genetic regulatory networks (GRNs)
switching parameters

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10.1109/TNNLS.2016.2636185

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title = "Stability analysis of genetic regulatory networks with switching parameters and time delays",

abstract = "This paper is concerned with the exponential stability analysis of genetic regulatory networks (GRNs) with switching parameters and time delays. In this paper, a new integral inequality and an improved reciprocally convex combination inequality are considered. By using the average dwell time approach together with a novel Lyapunov-Krasovskii functional, we derived some conditions to ensure the switched GRNs with switching parameters and time delays are exponentially stable. Finally, we give two numerical examples to clarify that our derived results are effective.",

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author = "Tingting Yu and Jianxing Liu and Yi Zeng and Xian Zhang and Qingshuang Zeng and Ligang Wu",

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AU - Liu, Jianxing

AU - Zeng, Yi

AU - Zhang, Xian

AU - Zeng, Qingshuang

AU - Wu, Ligang

PY - 2018/7

Y1 - 2018/7

N2 - This paper is concerned with the exponential stability analysis of genetic regulatory networks (GRNs) with switching parameters and time delays. In this paper, a new integral inequality and an improved reciprocally convex combination inequality are considered. By using the average dwell time approach together with a novel Lyapunov-Krasovskii functional, we derived some conditions to ensure the switched GRNs with switching parameters and time delays are exponentially stable. Finally, we give two numerical examples to clarify that our derived results are effective.

AB - This paper is concerned with the exponential stability analysis of genetic regulatory networks (GRNs) with switching parameters and time delays. In this paper, a new integral inequality and an improved reciprocally convex combination inequality are considered. By using the average dwell time approach together with a novel Lyapunov-Krasovskii functional, we derived some conditions to ensure the switched GRNs with switching parameters and time delays are exponentially stable. Finally, we give two numerical examples to clarify that our derived results are effective.

KW - Average dwell time

KW - delay partitioning

KW - exponential stability

KW - genetic regulatory networks (GRNs)

KW - switching parameters

UR - https://www.scopus.com/pages/publications/85023748214

U2 - 10.1109/TNNLS.2016.2636185

DO - 10.1109/TNNLS.2016.2636185

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JO - IEEE Transactions on Neural Networks and Learning Systems

JF - IEEE Transactions on Neural Networks and Learning Systems

IS - 7

ER -

Stability analysis of genetic regulatory networks with switching parameters and time delays

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Keywords

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