Asynchronous H∞ Dynamic Output Feedback Control for Markovian Jump Neural Networks with Time-varying Delays

Lin, Yuqian; Zhuang, Guangming; Xia, Jianwei; Sun, Wei; Zhao, Junsheng

doi:10.1007/s12555-021-0231-9

Cited by 11 publications

(4 citation statements)

References 53 publications

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“…Remark 3. We already knew that the research object of articles [2,3,53,54] was MJNNs, but we can see that these articles mainly discussed stability, state estimation, and synchronization research. Different from the method of constructing functionals in these articles, this article discusses a kind of FTS with quantization and actuator saturation problems, which is rare.…”

Section: Resultsmentioning

confidence: 99%

“…Because of their superiority in managing data and learning algorithms, neural networks (NNs) have piqued the interest of numerous academics in many domains over the past several decades. As a result, NNs have extensive applications in a variety of fields, including image processing, financial markets, combinatorial optimization, and fixed-point calculations [1][2][3]. Meanwhile, Markovian-jump time-varying delayed neural networks (MJTDNNs) are a type of neural network that incorporates a Markovian-jump process into their dynamics.…”

Section: Introductionmentioning

confidence: 99%

“…The Markovian-jump process is a mathematical model that describes how the system state changes over time, with different modes or states having different dynamics. The transmission of axonal signals usually creates delays in all neural networks, causing unexpected changing network phenomena, such as oscillation and instability [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

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Finite-Time Synchronization of Quantized Markovian-Jump Time-Varying Delayed Neural Networks via an Event-Triggered Control Scheme under Actuator Saturation

Saravanan¹,

Vadivel

Gunasekaran

2023

Mathematics

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In this paper, we present a finite-time synchronization (FTS) for quantized Markovian-jump time-varying delayed neural networks (QMJTDNNs) via event-triggered control. The QMJTDNNs take into account the effects of quantization on the system dynamics and utilize a combination of FTS and event-triggered communication to mitigate the effects of communication delays, quantization error, and efficient synchronization. We analyze the FTS and convergence properties of the proposed method and provide simulation results to demonstrate its effectiveness in synchronizing a network of QMJTDNNs. We introduce a new method to achieve the FTS of a system that has input constraints. The method involves the development of the Lyapunov–Krasovskii functional approach (LKF), novel integral inequality techniques, and some sufficient conditions, all of which are expressed as linear matrix inequalities (LMIs). Furthermore, the study presents the design of an event-triggered controller gain for a larger sampling interval. The effectiveness of the proposed method is demonstrated through numerical examples.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Finite-Time Synchronization of Quantized Markovian-Jump Time-Varying Delayed Neural Networks via an Event-Triggered Control Scheme under Actuator Saturation

Saravanan¹,

Vadivel

Gunasekaran

2023

Mathematics

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“…The basic idea is as follows: send the ciphered image to the receiver, which uses RDDNN (3) as the response system; (4) decrypt the image at the receiver based on the synchronization of the drive-response RDDNNs. The RDDNN models considered can be further extended to include complex-valued state variables [43], multiple time delays [44], and Markov switching parameters [45].…”

Section: Discussionmentioning

confidence: 99%

Energy-to-peak synchronization for uncertain reaction-diffusion delayed neural networks

et al. 2022

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This paper is devoted to energy-to-peak synchronization for uncertain reaction-diffusion delayed neural networks subject to external disturbances. The purpose is to determine a controller in such a way that the drive-response systems not only achieve asymptotical synchronization in the absence of disturbances but also possess a predefined energy-to-peak disturbance-rejection level under zero initial conditions. Through the use of Lyapunov-Krasovskii functionals and various integral inequalities, both delay-independent and dependent conditions are proposed in the form of linear matrix inequalities. When these conditions hold, the needed controller gains can be calculated directly. A numerical example is provided to show the applicability and reduced conservativeness of the present results.

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Observer-based security control for Markov jump systems under hybrid cyber-attacks and its application via event-triggered scheme

Tajudeen,

Ali,

Perumal

et al. 2023

Soft Comput

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Asynchronous H∞ Dynamic Output Feedback Control for Markovian Jump Neural Networks with Time-varying Delays

Cited by 11 publications

References 53 publications

Finite-Time Synchronization of Quantized Markovian-Jump Time-Varying Delayed Neural Networks via an Event-Triggered Control Scheme under Actuator Saturation

Finite-Time Synchronization of Quantized Markovian-Jump Time-Varying Delayed Neural Networks via an Event-Triggered Control Scheme under Actuator Saturation

Energy-to-peak synchronization for uncertain reaction-diffusion delayed neural networks

Observer-based security control for Markov jump systems under hybrid cyber-attacks and its application via event-triggered scheme

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