Event-triggered synchronization of delayed neural networks with actuator saturation using quantized measurements

Li, Liuwen; Zou, Wenlin; Fei, Shumin

doi:10.1016/j.jfranklin.2019.02.037

Cited by 29 publications

(11 citation statements)

References 36 publications

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“…Example 2. According to eorem 2 and considering system (1) with the following parameters, as shown in [1],…”

Section: Numerical Examplesmentioning

confidence: 99%

“…NNs represent a computing type similar to that of the brain [1]. Now, they have been widely applied in many fields, such as secure communications, combinatorial optimization, pattern recognition, associative memories, and complex system control [2][3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…Now, they have been widely applied in many fields, such as secure communications, combinatorial optimization, pattern recognition, associative memories, and complex system control [2][3][4][5][6][7][8]. At the same time, they are also the key feature of fitting nonlinear functions and recognizing patterns, which makes NNs can be specifically applied to aerospace, electronics, finance, automobile, banking, telecommunications, national defense, manufacturing, robotics, and transportation industries [1]. As we all know, the development of many systems is related not only to the current time state but also to the state before and after the current time point.…”

Section: Introductionmentioning

confidence: 99%

“…Considering the limited range of amplitude signals generated by actuators in practical systems, actuator saturation is a common phenomenon and widely considered in control systems [18]. Because actuator saturation is inevitable, it may lead to the deterioration of system performance, and even worse, it may make the closed-loop system unstable, so the research on actuator saturation is of great significance, and a lot of work has been done [1,9,[18][19][20][21][22][23]. For instance, in [1], using the quantized measurement method, the synchronization problem of DNNs was discussed with actuator saturation.…”

Section: Introductionmentioning

confidence: 99%

“…Because actuator saturation is inevitable, it may lead to the deterioration of system performance, and even worse, it may make the closed-loop system unstable, so the research on actuator saturation is of great significance, and a lot of work has been done [1,9,[18][19][20][21][22][23]. For instance, in [1], using the quantized measurement method, the synchronization problem of DNNs was discussed with actuator saturation. In [19], starting from the concept of master-slave synchronization, the author discussed the synchronization of drive-response systems with input delay and saturation.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Adaptive Event‐Triggered Synchronization of Networked Neural Networks with Time‐Varying Delay Subject to Actuator Saturation

Wang

et al. 2021

Complexity

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This paper discusses the adaptive event-triggered synchronization problem of a class of neural networks (NNs) with time-varying delay and actuator saturation. First, in view of the limited communication channel capacity of the network system and unnecessary data transmission in the NCSs, an adaptive event-triggered scheme (AETS) is introduced to reduce the network load and improve network utilization. Second, under the AETS, the synchronization error model of the delayed master-slave synchronization system is constructed with actuator saturation. Third, based on Lyapunov–Krasovskii functional (LKF), a new sufficient criterion to guarantee the asymptotic stability of the synchronization error system is derived. Moreover, by solving the stability criterion expressed in the form of a set of linear matrix inequalities (LMIs), some necessary parameters of the system are obtained. At last, two examples are expressed to demonstrate the feasibility of this method.

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“…Example 2. According to eorem 2 and considering system (1) with the following parameters, as shown in [1],…”

Section: Numerical Examplesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Adaptive Event‐Triggered Synchronization of Networked Neural Networks with Time‐Varying Delay Subject to Actuator Saturation

Wang

et al. 2021

Complexity

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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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Exponential synchronization of stochastic coupled neural networks with Markovian switching via event‐triggered control

Zhang

Chen

Tong

et al. 2022

Intl J Robust & Nonlinear

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This article reports on exponential synchronization for stochastic coupled neural networks (NNs) with mixed time‐varying delays, stochastic coupling strength, and Markovian switching. In order to reduce the amount of data transmission and save network resources, an event‐triggered control method is provided in this study. When the triggered condition can be met, the data can be transmitted so that the master and slave systems with limited resources and bandwidth can realize synchronization. By the Lyapunov stability theory and several analysis skills of matrix properties, some new criteria are obtained to make sure that stochastic coupled NNs are mean square exponential stability. These criteria are provided by linear matrix inequalities. Finally, a numerical case further demonstrates the validity of the proposed criteria.

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Event-triggered synchronization of delayed neural networks with actuator saturation using quantized measurements

Cited by 29 publications

References 36 publications

Adaptive Event‐Triggered Synchronization of Networked Neural Networks with Time‐Varying Delay Subject to Actuator Saturation

Adaptive Event‐Triggered Synchronization of Networked Neural Networks with Time‐Varying Delay Subject to Actuator Saturation

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

Exponential synchronization of stochastic coupled neural networks with Markovian switching via event‐triggered control

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