Robust Stability of Perturbed Continuous-Time Markov Chains

Lin LIN; James LAM; Zhiyi ZHONG; Xin YUAN; Ka-Wai KWOK; Guanrong CHEN

doi:10.1109/ICMLC66258.2025.11280202

Robust Stability of Perturbed Continuous-Time Markov Chains

Lin LIN
, James LAM
, Zhiyi ZHONG
, Xin YUAN
, Ka-Wai KWOK
, Guanrong CHEN

Department of Electrical Engineering

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

This paper studies the stability of perturbed continuous-time Markov chains (CT-MCs) by establishing stability criteria and robustness indicators. The robust stability criteria are derived from the spatial relationships between reachable sets and invariant subsets. Moreover, a robustness indicator is formulated to quantify the ability of each transition to resist perturbations affecting the stability, where lower values signify more critical transitions. Finally, a biological case study demonstrates the applicability of the theoretical result. This framework not only facilitates stability assessments for perturbed CT-MCs but also identifies critical transitions, the monitoring of which can enhance system resilience. © 2025 IEEE.

Original language	English
Title of host publication	Proceedings of 2025 International Conference on Machine Learning and Cybernetics
Publisher	IEEE
Pages	146-151
ISBN (Electronic)	9798331587369
ISBN (Print)	979-8-3315-8737-6
DOIs	https://doi.org/10.1109/ICMLC66258.2025.11280202
Publication status	Published - 2025
Event	24th International Conference on Machine Learning and Cybernetics (ICMLC 2025) - Holiday Inn Resort Baruna Ball, Bali, Indonesia Duration: 13 Jul 2025 → 15 Jul 2025 https://ieeexplore.ieee.org/xpl/conhome/11280001/proceeding

Publication series

Name	Proceedings - International Conference on Machine Learning and Cybernetics
ISSN (Print)	2160-133X
ISSN (Electronic)	2160-1348

Conference

Conference	24th International Conference on Machine Learning and Cybernetics (ICMLC 2025)
Place	Indonesia
City	Bali
Period	13/07/25 → 15/07/25
Internet address	https://ieeexplore.ieee.org/xpl/conhome/11280001/proceeding

Funding

This work was partially supported by the National Natural Science Foundation of China (62273286), GRF (17200124), the Shenzhen-Hong Kong-Macau Technology Research Programme (SGDX20230821091559019), the Guangdong Basic Research and Applied Research Fund (2024A1515011509), the Research Grants Council of Hong Kong (STG1/E-401/23- N, C4026-21G, 17209021), the Australian Research Council (DP240101140), and the Innovation and Technology Commission of the HKSAR Government under the InnoHK initiative.

Research Keywords

Continuous-time Markov chain
Invariant subset
Perturbation
Robustness
Stability

RGC Funding Information

RGC-funded

Access Output

10.1109/ICMLC66258.2025.11280202

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Check CityUHK Library

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Cite this

@inproceedings{15f14a5746204aae9dba27086f115c99,

title = "Robust Stability of Perturbed Continuous-Time Markov Chains",

abstract = "This paper studies the stability of perturbed continuous-time Markov chains (CT-MCs) by establishing stability criteria and robustness indicators. The robust stability criteria are derived from the spatial relationships between reachable sets and invariant subsets. Moreover, a robustness indicator is formulated to quantify the ability of each transition to resist perturbations affecting the stability, where lower values signify more critical transitions. Finally, a biological case study demonstrates the applicability of the theoretical result. This framework not only facilitates stability assessments for perturbed CT-MCs but also identifies critical transitions, the monitoring of which can enhance system resilience. {\textcopyright} 2025 IEEE.",

keywords = "Continuous-time Markov chain, Invariant subset, Perturbation, Robustness, Stability",

author = "Lin LIN and James LAM and Zhiyi ZHONG and Xin YUAN and Ka-Wai KWOK and Guanrong CHEN",

year = "2025",

doi = "10.1109/ICMLC66258.2025.11280202",

language = "English",

isbn = "979-8-3315-8737-6",

series = "Proceedings - International Conference on Machine Learning and Cybernetics",

publisher = "IEEE",

pages = "146--151",

booktitle = "Proceedings of 2025 International Conference on Machine Learning and Cybernetics",

address = "United States",

note = "24th International Conference on Machine Learning and Cybernetics (ICMLC 2025) ; Conference date: 13-07-2025 Through 15-07-2025",

url = "https://ieeexplore.ieee.org/xpl/conhome/11280001/proceeding",

}

LIN, L, LAM, J, ZHONG, Z, YUAN, X, KWOK, K-W & CHEN, G 2025, Robust Stability of Perturbed Continuous-Time Markov Chains. in Proceedings of 2025 International Conference on Machine Learning and Cybernetics. Proceedings - International Conference on Machine Learning and Cybernetics, IEEE, pp. 146-151, 24th International Conference on Machine Learning and Cybernetics (ICMLC 2025), Bali, Indonesia, 13/07/25. https://doi.org/10.1109/ICMLC66258.2025.11280202

Robust Stability of Perturbed Continuous-Time Markov Chains. / LIN, Lin; LAM, James; ZHONG, Zhiyi et al.
Proceedings of 2025 International Conference on Machine Learning and Cybernetics. IEEE, 2025. p. 146-151 (Proceedings - International Conference on Machine Learning and Cybernetics).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Robust Stability of Perturbed Continuous-Time Markov Chains

AU - LIN, Lin

AU - LAM, James

AU - ZHONG, Zhiyi

AU - YUAN, Xin

AU - KWOK, Ka-Wai

AU - CHEN, Guanrong

PY - 2025

Y1 - 2025

N2 - This paper studies the stability of perturbed continuous-time Markov chains (CT-MCs) by establishing stability criteria and robustness indicators. The robust stability criteria are derived from the spatial relationships between reachable sets and invariant subsets. Moreover, a robustness indicator is formulated to quantify the ability of each transition to resist perturbations affecting the stability, where lower values signify more critical transitions. Finally, a biological case study demonstrates the applicability of the theoretical result. This framework not only facilitates stability assessments for perturbed CT-MCs but also identifies critical transitions, the monitoring of which can enhance system resilience. © 2025 IEEE.

AB - This paper studies the stability of perturbed continuous-time Markov chains (CT-MCs) by establishing stability criteria and robustness indicators. The robust stability criteria are derived from the spatial relationships between reachable sets and invariant subsets. Moreover, a robustness indicator is formulated to quantify the ability of each transition to resist perturbations affecting the stability, where lower values signify more critical transitions. Finally, a biological case study demonstrates the applicability of the theoretical result. This framework not only facilitates stability assessments for perturbed CT-MCs but also identifies critical transitions, the monitoring of which can enhance system resilience. © 2025 IEEE.

KW - Continuous-time Markov chain

KW - Invariant subset

KW - Perturbation

KW - Robustness

KW - Stability

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DO - 10.1109/ICMLC66258.2025.11280202

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 979-8-3315-8737-6

T3 - Proceedings - International Conference on Machine Learning and Cybernetics

SP - 146

EP - 151

BT - Proceedings of 2025 International Conference on Machine Learning and Cybernetics

PB - IEEE

T2 - 24th International Conference on Machine Learning and Cybernetics (ICMLC 2025)

Y2 - 13 July 2025 through 15 July 2025

ER -

Robust Stability of Perturbed Continuous-Time Markov Chains

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Robust Stability of Perturbed Continuous-Time Markov Chains

Abstract

Publication series

Conference

Funding

Research Keywords

RGC Funding Information

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

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STG-ExtU-Lead: AI-assisted Microrobotic Platform for Minimally Invasive Interventions

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