Event-Based Robust Synchronization of Boolean Control Networks

Ouyang, Chen; Li, Lulu

doi:10.1109/tcsii.2019.2938588

Cited by 13 publications

(3 citation statements)

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“…Hence, it is considered a more competent sampling protocol. Due to its virtue, numerous attention has been drawn to this subject, which includes the stabilizing control tasks of single systems [13,14] and the consensus issues of networked systems, such as multiagent systems [15,16], complex networks [17,18], coupled neural networks [19][20][21], sensor networks [22], and Boolean networks [23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Partial-Information-Based Synchronization of Complex Networks with Multiple and Event-Triggered Couplings

2021

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We study the synchronization of complex networks by using event-sampling information. The nodes of the network are connected with event-triggered communication via multiple couplings. The couplings are split into several channels. Not all the channels are connected. Only a part of the states of each node can be communicated by the channels. An event detector is designed for each channel to independently determine the sampling moments. The couplings of the network are partial and event-triggered. Both features make that less information can be used for synchronization. The pinning controllers are also designed based on the sampled information. By establishing a time-dependent Lyapunov functional and utilizing an efficient event condition, we derive less conservative criteria for the synchronization of complex networks. Finally, the effectiveness of our main results is verified by an illustrative example, and comparisons are also presented to show how much conservatism can be reduced.

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Section: Introductionmentioning

confidence: 99%

Partial-Information-Based Synchronization of Complex Networks with Multiple and Event-Triggered Couplings

2021

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“…Secondly, calculate (δ 11 16 ) = ∪ 16 i=1 i (δ 11 16 ). We find that 1 (δ 11 16 ) = {δ 2 16 , δ 4 16 , δ 6 16 , δ 11 16 , δ 14 16 , δ 15 16 } and 2 (δ 11 16 ) = 3 (δ 11 16 ) = {δ 1 16 , δ 2 16 , δ 3 16 , δ 4 16 , δ 5 16 , δ 6 16 , δ 9 16 , δ 10 16 , δ 11 16 , δ 13 16 , δ 14 16 , δ 15 16 }.…”

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“…Hence, it holds that (δ 11 16 ) = {δ 1 16 , δ 2 16 , δ 3 16 , δ 4 16 , δ 5 16 , δ 6 16 , δ 9 16 , δ 10 16 , δ 11 16 , δ 13 16 , δ 14 16 , δ 15 16 }.…”

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Pinning Stabilization of Probabilistic Boolean Networks With Time Delays

Liu

Shi

et al. 2020

IEEE Access

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In this article, the stabilization issues for probabilistic Boolean Networks (PBNs) with time delays are discussed. This article's objective is designing an efficient algorithm to choose suitable nodes to be pinning controlled for PBNs with time delays. By using the semi-tensor product (STP) of matrices, a PBN with time delays can be converted into a discrete-time linear system, and the transition matrix also can be obtained. Then, the necessary and sufficient conditions in the form of algebraic expression are given for the existence and solvability of the pinning feedback controllers with minimum pinning nodes for PBNs with time delays. Besides, three algorithms are proposed for designing and solving minimum pinning controllers.INDEX TERMS Probabilistic Boolean networks, pinning control, time delays, semi-tensor product. I. INTRODUCTIONBoolean Networks (BNs), which were first proposed by Kauffman in 1969 [1], are a kind of logical dynamical models to describe gene regulatory networks (GRNs) [2]. As we all know, in a gene regulatory network, each gene can be expressed (1) or not expressed (0), which corresponds to binary state variables. A BN is a deterministic model to simulate the evolution of binary state variables. What's more, Boolean Networks have been widely studied in state estimation [3], logical networks [4], neural networks [5], etc. Recently, the STP of matrices was introduced by Cheng's team. With the help of STP, a BN can be transformed into a discrete-time linear system. Moreover, a logic function can be represented by an algebraic form with STP. This new matrix product was introduced to the study of BNs in many fields, such as the controllability, event-triggered control, ect., which have been studied in [6]-[11].To better handle of biological system uncertainty, Shmulevich etc. in [12] generalized the concept of BNs for application to probabilistic Boolean Networks (PBNs). In general, the PBNs can be seen as a kind of randomly switched BNs in given sets of BNs. Every BN is chosen with an definite probability. Many interesting results have been obtained for The associate editor coordinating the review of this manuscript and approving it for publication was Tingwen Huang.

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Finite-time output tracking of probabilistic Boolean control networks

Zhang

et al. 2021

Applied Mathematics and Computation

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Event-Based Robust Synchronization of Boolean Control Networks

Cited by 13 publications

References 21 publications

Partial-Information-Based Synchronization of Complex Networks with Multiple and Event-Triggered Couplings

Partial-Information-Based Synchronization of Complex Networks with Multiple and Event-Triggered Couplings

Pinning Stabilization of Probabilistic Boolean Networks With Time Delays

Finite-time output tracking of probabilistic Boolean control networks

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