Finite-Time Stabilizers for Large-Scale Stochastic Boolean Networks

IF 9.4 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Lin Lin;James Lam;Wai-Ki Ching;Qian Qiu;Liangjie Sun;Bo Min
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引用次数: 0

Abstract

This article presents a distributed pinning control strategy aimed at achieving global stabilization of Markovian jump Boolean control networks. The strategy relies on network matrix information to choose controlled nodes and adopts the algebraic state space representation approach for designing pinning controllers. Initially, a sufficient criterion is established to verify the global stability of a given Markovian jump Boolean network (MJBN) with probability one at a specific state within finite time. To stabilize an unstable MJBN at a predetermined state, the selection of pinned nodes involves removing the minimal number of entries, ensuring that the network matrix transforms into a strictly lower (or upper) triangular form. For each pinned node, two types of state feedback controllers are developed: 1) mode-dependent and 2) mode-independent, with a focus on designing a minimally updating controller. The choice of controller type is determined by the feasibility condition of the mode-dependent pinning controller, which is articulated through the solvability of matrix equations. Finally, the theoretical results are illustrated by studying the T cell large granular lymphocyte survival signaling network consisting of 54 genes and 6 stimuli.
大规模随机布尔网络的有限时间稳定器。
针对马尔可夫跳变布尔控制网络的全局镇定问题,提出了一种分布式钉住控制策略。该策略依靠网络矩阵信息选择被控节点,采用代数状态空间表示方法设计固定控制器。首先,建立了一个充分的准则来验证给定概率为1的马尔可夫跳布尔网络(MJBN)在有限时间内特定状态下的全局稳定性。为了将不稳定的MJBN稳定在预定状态,固定节点的选择涉及删除最小数量的条目,确保网络矩阵转换为严格的下(或上)三角形形式。针对每个固定节点,开发了两种类型的状态反馈控制器:1)模式相关和2)模式无关,并重点设计了最小更新控制器。控制器类型的选择取决于模态相关固定控制器的可行性条件,并通过矩阵方程的可解性来表达。最后,通过研究由54个基因和6种刺激组成的T细胞大颗粒淋巴细胞生存信号网络来说明理论结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Transactions on Cybernetics
IEEE Transactions on Cybernetics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, CYBERNETICS
CiteScore
25.40
自引率
11.00%
发文量
1869
期刊介绍: The scope of the IEEE Transactions on Cybernetics includes computational approaches to the field of cybernetics. Specifically, the transactions welcomes papers on communication and control across machines or machine, human, and organizations. The scope includes such areas as computational intelligence, computer vision, neural networks, genetic algorithms, machine learning, fuzzy systems, cognitive systems, decision making, and robotics, to the extent that they contribute to the theme of cybernetics or demonstrate an application of cybernetics principles.
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