Dynamic Event-Based Non-Fragile Dissipative State Estimation for Quantized Complex Networks With Fading Measurements and Its Application

Fan, Sha; Yan, Huaicheng; Zhang, Hao; Shen, Hao; Shi, Kaibo

doi:10.1109/tcsi.2020.3036626

Cited by 37 publications

(20 citation statements)

References 33 publications

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“…Therefore, research on network topology is a prerequisite for analyzing and predicting the real-time performance of complex real-time systems. [20][21][22][23] However, the above methods do not consider the real-time performance during the generation of the network topology. For a typical complex real-time network, such as the avionics network, its different network topologies will seriously affect the real-time performance of the whole network.…”

Section: Related Workmentioning

confidence: 99%

“…Poor real‐time performance of the network can cause a series of serious consequences for the whole system, such as power interruption for power system, task execution failure for avionics system, operational failure for high‐speed rail systems. Therefore, research on network topology is a prerequisite for analyzing and predicting the real‐time performance of complex real‐time systems 20–23 . However, the above methods do not consider the real‐time performance during the generation of the network topology.…”

Section: Introductionmentioning

confidence: 99%

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Network topology generation based on eigenvector centrality with real‐time guarantee

Wang

Xiao-yan

2022

Concurrency and Computation

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The real‐time performance is a major concern of complex real‐time systems during information acquisition, transmission, processing, and application. The interactions among information within these systems constitute complicated information exchange networks. Network topology plays an important role in the behaviors of information interactions and interferes, which severely affects the real‐time performance of the whole systems. Methods such as synchronization‐based method or traffic balancing strategy are the main ideas that guide the construction of network topology. However, these methods could not consider the real‐time performance in the process of topology construction. This article proposes an automatic topology generation algorithm with real‐time guarantee. Specifically speaking, the nodes in the network are clustered to generate the network topology by introducing the concepts of node degree and eigenvector centrality to ensure the real‐time performance of the network. The position of the nodes in the network is determined by comparing the node degree and the communication factors. Analytic method and simulation method are used to verify the real‐time performance of the proposed algorithm. Results show that the real‐time performance of more than half of the total information exchanges is improved compared with the traffic balancing method and topology grouping method for an industrial‐scale networking case.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Network topology generation based on eigenvector centrality with real‐time guarantee

Wang

Xiao-yan

2022

Concurrency and Computation

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“…3,[5][6][7][8] At the same instant, it is well realized that acquiring accurate states of the underlying NN is essential in enabling NNs to perform specific tasks, such as system modeling, classification, approximation, and control problems. 3,9 However, one could not obtain all the information of network's states due to intrinsic properties of NNs such as high dimensions, complicated interconnections, high nonlinearities, and resource restrictions. As a result, the problem of state estimation exists in the literature, where the key objective is to estimate the states of NN using available but possibly noisy or imperfect information from the network measurements.…”

Section: Introductionmentioning

confidence: 99%

Event‐triggered nonfragile state estimation for delayed neural networks with additive and multiplicative gain variations

Karnan

Nagamani

2023

Intl J Robust & Nonlinear

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This article aims to design a nonfragile state estimator for neural networks with time-varying delay under the event-triggered mechanism. This paper addresses the two most common issues that exist in large-scale networks: gain variations in the estimator design and lack of network resources. Based on the nonfragile paradigm and an event-triggered mechanism, an estimator is being developed to address this issue, considering both the additive and multiplicative structured gain variations. Through the Lyapunov-Krasovskii functional method and linear matrix inequality technique, sufficient conditions are derived to guarantee the existence of the proposed state estimator by employing the generalized free-weighting matrix inequality and improved reciprocally convex inequality.Moreover, the obtained linear matrix inequalities provide an explicit expression of the estimator gain matrix and triggering matrix. Eventually, two numerical examples are provided to authenticate the proposed findings.

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“…Owing to its complex inherent structure, most systems in real life can be regarded simply as complex networks, including, but not limited to social networks, biological networks, power grid networks, and Internet [1][2][3]. Consequently, considerable research interest has been stirred over the past few decades and there has been a host of meaningful published achievements of complex networks [4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Event‐Based State Estimation for Networked Singularly Perturbed Complex Networks

Ren

2022

Complexity

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This paper deals with the multievent-triggering-based state estimation for a class of discrete-time networked singularly perturbed complex networks (SPCNs). A small singularly perturbed scalar is adopted to establish a discrete-time SPCNs model. To reduce the communication burdens, the data transmission between the sensor and the estimator is managed by a multievent generator function. Depending on the singularly-perturbed-based Lyapunov theory, a sufficient condition is constructed to guarantee that the estimation error is exponentially ultimately bounded in the mean square. Finally, the validity of the developed result is demonstrated by a simulation example.

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Dynamic Event-Based Non-Fragile Dissipative State Estimation for Quantized Complex Networks With Fading Measurements and Its Application

Cited by 37 publications

References 33 publications

Network topology generation based on eigenvector centrality with real‐time guarantee

Network topology generation based on eigenvector centrality with real‐time guarantee

Event‐triggered nonfragile state estimation for delayed neural networks with additive and multiplicative gain variations

Event‐Based State Estimation for Networked Singularly Perturbed Complex Networks

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