Asymptotical Stability and Stabilization of Continuous-Time Probabilistic Logic Networks
| Autor: | Yuqian Guo, Yang Liu, Weihua Gui, Zhitao Li |
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| Rok vydání: | 2022 |
| Předmět: |
Convergence of random variables
Control and Systems Engineering Computer science Stability (learning theory) Probabilistic logic Gene regulatory network State (computer science) Electrical and Electronic Engineering Type (model theory) Invariant (mathematics) Topology Transition rate matrix Computer Science Applications |
| Zdroj: | IEEE Transactions on Automatic Control. 67:279-291 |
| ISSN: | 2334-3303 0018-9286 |
| DOI: | 10.1109/tac.2021.3063130 |
| Popis: | Discrete-time probabilistic logic networks (DT-PLNs), of which probabilistic Boolean networks (PBNs) are a special type, are an important qualitative model for gene regulatory networks (GRNs). Although a DT-PLN can predict the long-term behavior of a GRN, using it to describe the transient kinetics at the micro-timescale level remains inconvenient. In this study, we investigate the problems associated with the stability and stabilization of continuous-time probabilistic logic networks (CT-PLNs). First, we demonstrate that the concept of finite-time stability for DT-PLNs cannot be extended to CT-PLNs owing to the nonsingularity of transitional probability matrices (TPMs). Thus, we introduce the concept of asymptotical stability, which is defined as the convergence in distribution of the network state. Second, by developing the theory of invariant subsets for CT-PLNs, a necessary and sufficient condition for asymptotical stability with respect to a subset is proposed, which is expressed in terms of the transition rate matrix (TRM) of probability. Third, for a CT-PLN with input nodes, termed a continuous-time probabilistic logic control network (CT-PLCN), we discuss the subsets that a |
| Databáze: | OpenAIRE |
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