Robust Exponential Synchronization for Stochastic Delayed Neural Networks with Reaction–Diffusion Terms and Markovian Jumping Parameters

Tengda Wei; Yangfan Wang; Linshan Wang

doi:10.1007/s11063-017-9756-6

Robust Exponential Synchronization for Stochastic Delayed Neural Networks with Reaction–Diffusion Terms and Markovian Jumping Parameters

Tengda Wei, Yangfan Wang (Lead / Corresponding author), Linshan Wang (Lead / Corresponding author)

Mathematics

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

6 Citations (Scopus)

Abstract

This paper investigates robust exponential synchronization for stochastic delayed neural networks with reaction–diffusion terms and Markovian jumping parameters driven by infinite dimensional Wiener processes. The novelty of this paper lives in the use of a new Lyapunov–Krasovskii functional and Poincaré inequality to present some criteria for robust exponential synchronization in terms of linear matrix inequalities (LMIs) and matrix measure under Robin boundary conditions. Finally, two numerical examples are provided to illustrate the effectiveness of the easily verifiable synchronization LMIs in MATLAB toolbox.

Original language	English
Pages (from-to)	979–994
Number of pages	16
Journal	Neural Processing Letters
Volume	48
Early online date	21 Nov 2017
DOIs	https://doi.org/10.1007/s11063-017-9756-6
Publication status	Published - 2018

Keywords

Markovian jumping parameter
Reaction–diffusion
Stochastic delayed neural network
Synchronization
Wiener process

ASJC Scopus subject areas

Software
General Neuroscience
Computer Networks and Communications
Artificial Intelligence

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10.1007/s11063-017-9756-6

Cite this

@article{b7fc7a1e94604da08c5e38bf6c4f41c6,

title = "Robust Exponential Synchronization for Stochastic Delayed Neural Networks with Reaction–Diffusion Terms and Markovian Jumping Parameters",

abstract = "This paper investigates robust exponential synchronization for stochastic delayed neural networks with reaction–diffusion terms and Markovian jumping parameters driven by infinite dimensional Wiener processes. The novelty of this paper lives in the use of a new Lyapunov–Krasovskii functional and Poincar{\'e} inequality to present some criteria for robust exponential synchronization in terms of linear matrix inequalities (LMIs) and matrix measure under Robin boundary conditions. Finally, two numerical examples are provided to illustrate the effectiveness of the easily verifiable synchronization LMIs in MATLAB toolbox.",

keywords = "Markovian jumping parameter, Reaction–diffusion, Stochastic delayed neural network, Synchronization, Wiener process",

author = "Tengda Wei and Yangfan Wang and Linshan Wang",

note = "Funding for this research was provided by: National Natural Science Foundation of China (11771014, 31772844) Natural Science Foundation of Shandong Province (ZR2011AZ001) National Natural Science Foundation of China (CN) (31302182, 11171374)",

year = "2018",

doi = "10.1007/s11063-017-9756-6",

language = "English",

volume = "48",

pages = "979–994",

journal = "Neural Processing Letters",

issn = "1370-4621",

publisher = "Springer Verlag",

}

Robust Exponential Synchronization for Stochastic Delayed Neural Networks with Reaction–Diffusion Terms and Markovian Jumping Parameters. / Wei, Tengda; Wang, Yangfan (Lead / Corresponding author); Wang, Linshan (Lead / Corresponding author).
In: Neural Processing Letters, Vol. 48, 2018, p. 979–994.

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

TY - JOUR

T1 - Robust Exponential Synchronization for Stochastic Delayed Neural Networks with Reaction–Diffusion Terms and Markovian Jumping Parameters

AU - Wei, Tengda

AU - Wang, Yangfan

AU - Wang, Linshan

N1 - Funding for this research was provided by: National Natural Science Foundation of China (11771014, 31772844) Natural Science Foundation of Shandong Province (ZR2011AZ001) National Natural Science Foundation of China (CN) (31302182, 11171374)

PY - 2018

Y1 - 2018

N2 - This paper investigates robust exponential synchronization for stochastic delayed neural networks with reaction–diffusion terms and Markovian jumping parameters driven by infinite dimensional Wiener processes. The novelty of this paper lives in the use of a new Lyapunov–Krasovskii functional and Poincaré inequality to present some criteria for robust exponential synchronization in terms of linear matrix inequalities (LMIs) and matrix measure under Robin boundary conditions. Finally, two numerical examples are provided to illustrate the effectiveness of the easily verifiable synchronization LMIs in MATLAB toolbox.

AB - This paper investigates robust exponential synchronization for stochastic delayed neural networks with reaction–diffusion terms and Markovian jumping parameters driven by infinite dimensional Wiener processes. The novelty of this paper lives in the use of a new Lyapunov–Krasovskii functional and Poincaré inequality to present some criteria for robust exponential synchronization in terms of linear matrix inequalities (LMIs) and matrix measure under Robin boundary conditions. Finally, two numerical examples are provided to illustrate the effectiveness of the easily verifiable synchronization LMIs in MATLAB toolbox.

KW - Markovian jumping parameter

KW - Reaction–diffusion

KW - Stochastic delayed neural network

KW - Synchronization

KW - Wiener process

U2 - 10.1007/s11063-017-9756-6

DO - 10.1007/s11063-017-9756-6

M3 - Article

AN - SCOPUS:85034630383

SN - 1370-4621

VL - 48

SP - 979

EP - 994

JO - Neural Processing Letters

JF - Neural Processing Letters

ER -

Robust Exponential Synchronization for Stochastic Delayed Neural Networks with Reaction–Diffusion Terms and Markovian Jumping Parameters

Abstract

Keywords

ASJC Scopus subject areas

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