Finite-Time Synchronization of Markovian Coupled Neural Networks With Delays via Intermittent Quantized Control: Linear Programming Approach

Tang, Rongqiang; Su, Housheng; Zou, Yi; Yang, Xinsong

doi:10.1109/tnnls.2021.3069926

Cited by 81 publications

(24 citation statements)

References 55 publications

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“…Remark 1 Recently, the various synchronization modes [15,27,[33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51] were investigated such as finite-time synchronization [34,45,46], asymptotic synchronization [15,[35][36][37], Mittag-Leffler synchronization [38,39,41,44] and projective synchronization [40,42,43]. The different control strategies had been designed to solve synchronization problems for NNs including impulsive control [17,20,23,38,39,47], linear feedback control [44] and sliding mode control [40].…”

Section: Resultsmentioning

confidence: 99%

Novel algebraic criteria on global Mittag–Leffler synchronization for FOINNs with the Caputo derivative and delay

Yu-hong

Zhang

et al. 2021

J. Appl. Math. Comput.

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This paper focuses on the global Mittag-Leffler (M-L) synchronization for fractionalorder inertial neural networks (FOINNs) including the Caputo derivative and delay. By choosing an appropriate variable transformation, the considered system including the inertial term is transformed into two fractional-order nonlinearly coupled interconnected subsystems. Combining with the delayed-feedback controller, fractional Lyapunov functional approach and inequality analysis technique, multi sets of novel algebraic criteria on the global M-L synchronization between derive-response systems are established. The main prominent highlight of this paper is that the proposed results are characterized by the algebraic inequalities form, which can simplify the calculation and facilitate the test. Two numerical simulation examples validate the theoretical results.

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Section: Resultsmentioning

confidence: 99%

Novel algebraic criteria on global Mittag–Leffler synchronization for FOINNs with the Caputo derivative and delay

Yu-hong

Zhang

et al. 2021

J. Appl. Math. Comput.

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“…Numerical simulation verified the validity of the results obtained here. At present, by using the aperiodically intermittent control with improved conditions(λ(t 2k+1 − t 2k )/ε(t 2k+2 − t 2k+1 ) � χ k 〉1), many excellent research results on finitetime tracking of uncertain nonlinear systems [36] and finite-time synchronization of delayed neural networks have emerged [11,19,20,24]. Note that, time delay or distributed delay, as one of the vital factors affecting the dynamic behaviors of neural networks, cannot be neglected [8].…”

Section: Discussionmentioning

confidence: 99%

“…Remark 5. From condition (24) one can see that, for fixed value ](t 2k+2 − t 2k+1 ), the value of the control interval t 2k+1 − t 2k can be decreased when the value μ is large as long as χ k > 1 is satisfied. at is, by tuning the value of the control gain D such that the value of μ increase or decrease.…”

Section: Computational Intelligence and Neurosciencementioning

confidence: 99%

“…However, the above-given existing aperiodically intermittent control is complex and some strict restrictions are imposed on the control width and noncontrol width, which make it difficult to be implemented in practice. It is demonstrated that new aperiodically intermittent control methods [ 24 ] are proposed to improve the above existing control. Thus, adopting a new control strategy to study the asymptotic synchronization of multilayer delayed networks is necessary.…”

Section: Introductionmentioning

confidence: 99%

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New Criteria for Synchronization of Multilayer Neural Networks via Aperiodically Intermittent Control

Jing

Zhang

2022

Computational Intelligence and Neuroscience

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In this paper, the globally asymptotic synchronization of multi-layer neural networks is studied via aperiodically intermittent control. Due to the property of intermittent control, it is very hard to deal with the effect of time-varying delays and ascertain the control and rest widths for intermittent control. A new lemma with generalized Halanay-type inequalities are proposed first. Then, by constructing a new Lyapunov–Krasovskii functional and utilizing linear programming methods, several useful criteria are derived to ensure the multilayer neural networks achieve asymptotic synchronization. Moreover, an aperiodically intermittent control is designed, which has no direct relationship with control widths and rest widths and extends existing aperiodically intermittent control techniques, the control gains are designed by solving the linear programming. Finally, a numerical example is provided to confirm the effectiveness of the proposed theoretical results.

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“…Based on these, FTBS of MNNs with the saturation function is studied in this article. References [32]- [34] studied the FTS of complex networks or neural networks; however, they ignored the optimization of control parameters.…”

mentioning

confidence: 99%

The Optimization of Control Parameters: Finite-Time Bipartite Synchronization of Memristive Neural Networks With Multiple Time Delays via Saturation Function

Chang

Park²,

Yang³

2023

IEEE Trans. Neural Netw. Learning Syst.

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This article studies the memristive neural networks with multiple time delays (MNNsMTDs). The topology of networks is signed, which contains both cooperative and competitive relationships. Two controllers without time delays are designed to achieve finite-time bipartite synchronization (FTBS) and practical FTBS (PFTBS) of MNNsMTDs. A novel controller with a saturation function rather than a sign function is proposed to avoid chattering. Along with the Lyapunov function method, some mathematical techniques, and scaling inequalities, some sufficient conditions for FTBS and PFTBS of MNNsMTDs are attained. Besides, this article also concerns fixed-time bipartite synchronization (FXBS) and practical FXBS (PFXBS) of MNNsMTDs. An optimization model is designed to obtain some optimal control parameters. An algorithm based on particle swarm optimization (PSO) is provided to solve this model. Some numerical examples are included to demonstrate the correctness and applicability of the approaches.

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Finite-Time Synchronization of Markovian Coupled Neural Networks With Delays via Intermittent Quantized Control: Linear Programming Approach

Cited by 81 publications

References 55 publications

Novel algebraic criteria on global Mittag–Leffler synchronization for FOINNs with the Caputo derivative and delay

Novel algebraic criteria on global Mittag–Leffler synchronization for FOINNs with the Caputo derivative and delay

New Criteria for Synchronization of Multilayer Neural Networks via Aperiodically Intermittent Control

The Optimization of Control Parameters: Finite-Time Bipartite Synchronization of Memristive Neural Networks With Multiple Time Delays via Saturation Function

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