Nonfragile Dissipative Synchronization for Markovian Memristive Neural Networks: A Gain-Scheduled Control Scheme

Hao SHEN; Ting WANG; Jinde CAO; Guoping LU; Yongduan SONG; Tingwen HUANG

doi:10.1109/TNNLS.2018.2874035

Nonfragile Dissipative Synchronization for Markovian Memristive Neural Networks: A Gain-Scheduled Control Scheme

Hao SHEN
, Ting WANG
, Jinde CAO^*
, Guoping LU
, Yongduan SONG
, Tingwen HUANG

^*Corresponding author for this work

Research output: Journal Publications › Journal Article (refereed) › peer-review

154 Citations (Scopus)

Abstract

In this paper, the dissipative synchronization control problem for Markovian jump memristive neural networks (MNNs) is addressed with fully considering the time-varying delays and the fragility problem in the process of implementing the gain-scheduled controller. A Markov jump model is introduced to describe the stochastic changing among the connection of MNNs and it makes the networks under consideration suitable for some actual circumstances. By utilizing some improved integral inequalities and constructing a proper Lyapunov-Krasovskii functional, several delay-dependent synchronization criteria with less conservatism are established to ensure the dynamic error system is strictly stochastically dissipative. Based on these criteria, the procedure of designing the desired nonfragile gain-scheduled controller is established, which can well handle the fragility problem in the process of implementing the controller. Finally, an illustrated example is employed to explain that the developed method is efficient and available.

Original language	English
Article number	8515236
Pages (from-to)	1841-1853
Number of pages	13
Journal	IEEE Transactions on Neural Networks and Learning Systems
Volume	30
Issue number	6
Early online date	30 Oct 2018
DOIs	https://doi.org/10.1109/TNNLS.2018.2874035
Publication status	Published - Jun 2019
Externally published	Yes

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 61873002, Grant 61703004, Grant 61833005, Grant 61860206008, and Grant 61773081, in part by the China Postdoctoral Science Foundation under Grant 2018M632206, in part by the National Natural Science Foundation of Anhui Province under Grant 1708085MF165 and Grant 1808085QA18, in part by the Technology Transformation Program of Chongqing Higher Education University under Grant KJZH17102, in part by the National Priority Research Project through Qatar National Research Fund under Grant NPRP 9 166-1-031, and in part by the Jiangsu Provincial Key Laboratory of Networked Collective Intelligence under Grant BM2017002.

Keywords

Dissipative synchronization
gain-scheduled control
Markovian jump memristive neural networks (MJMNNs)
time-varying delays (TVDs)

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10.1109/TNNLS.2018.2874035

Cite this

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title = "Nonfragile Dissipative Synchronization for Markovian Memristive Neural Networks: A Gain-Scheduled Control Scheme",

abstract = "In this paper, the dissipative synchronization control problem for Markovian jump memristive neural networks (MNNs) is addressed with fully considering the time-varying delays and the fragility problem in the process of implementing the gain-scheduled controller. A Markov jump model is introduced to describe the stochastic changing among the connection of MNNs and it makes the networks under consideration suitable for some actual circumstances. By utilizing some improved integral inequalities and constructing a proper Lyapunov-Krasovskii functional, several delay-dependent synchronization criteria with less conservatism are established to ensure the dynamic error system is strictly stochastically dissipative. Based on these criteria, the procedure of designing the desired nonfragile gain-scheduled controller is established, which can well handle the fragility problem in the process of implementing the controller. Finally, an illustrated example is employed to explain that the developed method is efficient and available.",

keywords = "Dissipative synchronization, gain-scheduled control, Markovian jump memristive neural networks (MJMNNs), time-varying delays (TVDs)",

author = "Hao SHEN and Ting WANG and Jinde CAO and Guoping LU and Yongduan SONG and Tingwen HUANG",

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AU - SHEN, Hao

AU - WANG, Ting

AU - CAO, Jinde

AU - LU, Guoping

AU - SONG, Yongduan

AU - HUANG, Tingwen

PY - 2019/6

Y1 - 2019/6

N2 - In this paper, the dissipative synchronization control problem for Markovian jump memristive neural networks (MNNs) is addressed with fully considering the time-varying delays and the fragility problem in the process of implementing the gain-scheduled controller. A Markov jump model is introduced to describe the stochastic changing among the connection of MNNs and it makes the networks under consideration suitable for some actual circumstances. By utilizing some improved integral inequalities and constructing a proper Lyapunov-Krasovskii functional, several delay-dependent synchronization criteria with less conservatism are established to ensure the dynamic error system is strictly stochastically dissipative. Based on these criteria, the procedure of designing the desired nonfragile gain-scheduled controller is established, which can well handle the fragility problem in the process of implementing the controller. Finally, an illustrated example is employed to explain that the developed method is efficient and available.

AB - In this paper, the dissipative synchronization control problem for Markovian jump memristive neural networks (MNNs) is addressed with fully considering the time-varying delays and the fragility problem in the process of implementing the gain-scheduled controller. A Markov jump model is introduced to describe the stochastic changing among the connection of MNNs and it makes the networks under consideration suitable for some actual circumstances. By utilizing some improved integral inequalities and constructing a proper Lyapunov-Krasovskii functional, several delay-dependent synchronization criteria with less conservatism are established to ensure the dynamic error system is strictly stochastically dissipative. Based on these criteria, the procedure of designing the desired nonfragile gain-scheduled controller is established, which can well handle the fragility problem in the process of implementing the controller. Finally, an illustrated example is employed to explain that the developed method is efficient and available.

KW - Dissipative synchronization

KW - gain-scheduled control

KW - Markovian jump memristive neural networks (MJMNNs)

KW - time-varying delays (TVDs)

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DO - 10.1109/TNNLS.2018.2874035

M3 - Journal Article (refereed)

AN - SCOPUS:85055868942

SN - 2162-237X

VL - 30

SP - 1841

EP - 1853

JO - IEEE Transactions on Neural Networks and Learning Systems

JF - IEEE Transactions on Neural Networks and Learning Systems

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M1 - 8515236

ER -

Nonfragile Dissipative Synchronization for Markovian Memristive Neural Networks: A Gain-Scheduled Control Scheme

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