Fixed-time synchronization of Markovian jump fuzzy cellular neural networks with stochastic disturbance and time-varying delays

Cui, Wenxia; Wang, Zhenjie; Jin, Wenbin

doi:10.1016/j.fss.2020.05.007

Cited by 32 publications

(15 citation statements)

References 31 publications

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“…Remark 3. From the adaptive law (12), we know that the dynamic threshold 𝜕 i (t) is dynamically adjusted depending on the error e ki (t). In particular, when the system tends to be stable, the error e ki (t) tends to 0, which means, ∂i (t) tends to 0, then the dynamic threshold 𝜕 i (t) is kept constant.…”

Section: Problem Model Formulation and Preliminariesmentioning

confidence: 99%

“…Theorem 1. For given positive scalars d M , d m , 𝜏 M , 𝜏 m , α and 𝜅 i > 0, 𝜕 i ∈ (0, 1), the augmented system ( 15) is asymptotically stable with a given H∞ performance index 𝛾 > 0 under the AETS (10), if there exist real matrices 11,12), such that…”

Section: H∞ Performance Analysismentioning

confidence: 99%

“…It is worth noting that the system modes may jump from one mode to another at different times, and Markov process can simulate this process well. Therefore, based on the analysis and synthesis method of Markovian jump systems (MJSs), great progress has been made in the research of Markovian jump NNs 8‐12 . There is no doubt that the results based on Markov jump are conservative in some senses.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, based on the analysis and synthesis method of Markovian jump systems (MJSs), great progress has been made in the research of Markovian jump NNs. [8][9][10][11][12] There is no doubt that the results based on Markov jump are conservative in some senses. Because the duration between successive mode transitions determines the Markov process, and most jump times has constant transition rate (TR) and obeys exponential distribution.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Adaptive event‐triggered H∞ state estimation of semi‐Markovian jump neural networks with randomly occurred sensor nonlinearity

Song

et al. 2022

Intl J Robust & Nonlinear

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This article mainly discusses the problem for adaptive event-triggered H∞ state estimation of semi-Markovian jump neural networks (s-MJNNs) subject to random sensor nonlinearity. To reduce the communication load, adaptive event-triggered scheme (AETS) is introduced to decide whether to transmit sampled data or not. Also, considering the possible sensor nonlinearity, a new estimation error model is established under the framework of AETS. An appropriate Lyapunov-Krasovskii functional (LKF) containing the proposed adaptive event trigger condition is constructed, and sufficient conditions are obtained to guarantee the asymptotic stability of the estimation error system. Then, through a set of feasible linear matrix inequalities (LMIs), the co-design method of estimator and AETS is proposed. Finally, the feasibility of this paper is proved by three numerical examples.

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Section: Problem Model Formulation and Preliminariesmentioning

confidence: 99%

Section: H∞ Performance Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive event‐triggered H∞ state estimation of semi‐Markovian jump neural networks with randomly occurred sensor nonlinearity

Song

et al. 2022

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

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“…And in related study, sufficient conditions for finite time synchronization bounds are derived according to Markov observations, thus verifying the finite time synchronization of coupled neural networks with hopping internal coupling and non-fragile controllers [8]. Cui et al [3] considered that the fixedtime synchronization of Markov jumping fuzzy neural networks with random disturbance and leakage time-varying delays was studied by designing time-dependent controllers with or without fuzzy terms.…”

Section: Introductionmentioning

confidence: 96%

Finite time Synchronization of Inertial Memristive Neural Networks with Time Varying Delay

Pang

Dong²

2021

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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Finite time synchronization control of inertial memristor-based neural networks with varying delay is considered. In view of drive and response concept, the sufficient conditions to ensure finite time synchronization issue of inertial memristive neural networks is given. Based on Lyapunov finite time asymptotic theory, a kind of feedback controllers is designed for inertial memristorbased neural networks to realize the finite time synchronization. Based on Lyapunov stability theory, close loop error system can be proved finite time and fixed time stable. Finally, illustrative example is given to illustrate the effectiveness of theoretical results.

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Robust fixed‐time synchronization of fuzzy shunting‐inhibitory cellular neural networks with feedback and adaptive control

Liu,

Pu,

Hua

et al. 2024

Adaptive Control & Signal

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This article addresses the robust fixed‐time synchronization of fuzzy shunting‐inhibitory cellular neural networks (FSICNNs) with delays by utilizing two different types of control strategies. First, a feedback controller is proposed to achieve fixed‐time synchronization of FSICNNs. Secondly, a novel adaptive controller is designed to guarantee fixed‐time synchronization of FSICNNs, automatically adjusting all control gains without the need for advanced settings. The use of differential inequality techniques and the Lyapunov function method yields several sufficient conditions to ensure fixed‐time synchronization for the considered FSICNNs. Finally, an example, along with its numerical simulation, is presented to demonstrate the validity of the proposed theoretical results.

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Fixed-time synchronization of Markovian jump fuzzy cellular neural networks with stochastic disturbance and time-varying delays

Cited by 32 publications

References 31 publications

Adaptive event‐triggered H∞ state estimation of semi‐Markovian jump neural networks with randomly occurred sensor nonlinearity

Adaptive event‐triggered H∞ state estimation of semi‐Markovian jump neural networks with randomly occurred sensor nonlinearity

Finite time Synchronization of Inertial Memristive Neural Networks with Time Varying Delay

Robust fixed‐time synchronization of fuzzy shunting‐inhibitory cellular neural networks with feedback and adaptive control

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