Induced-Equations-Based Stability Analysis and Stabilization of Markovian Jump Boolean Networks

Zhu, Shiyong; Lu, Jianquan; Lou, Yijun; Liu, Yang

doi:10.1109/tac.2020.3037142

Cited by 39 publications

(6 citation statements)

References 33 publications

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“…It is expected that the similar DP and shortest path-based approaches are applicable. Furthermore, from the viewpoint of the model structure, the estimation problems of other stochastic models for the BCNs, including BCNs with Markovian jump [38], are further examined.…”

Section: Future Recommendationsmentioning

confidence: 99%

Maximum-Likelihood State Estimators in Probabilistic Boolean Control Networks

Toyoda

2023

IEEE Trans. Cybern.

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This study addresses state estimation problems for probabilistic Boolean control networks (PBCNs). Compared with deterministic Boolean networks, PBCNs have the stochastic switching in logical update functions in the state equation. Consequently, statistical analysis is required to estimate unavailable states, which induces an optimization problem called maximum-likelihood estimation. This article mainly focuses on two scenarios: 1) state estimation from partially measured state and 2) state estimation from output data, meaning observer design. The resulting optimization problems are solved using efficient algorithms based on dynamic programming. Concurrently, Dijkstra-type algorithms, which solve equivalent shortest path problems, are also proposed using best-first search. Furthermore, both the proposed algorithms derive novel observer design methods for PBCNs. The proposed algorithms are evaluated with practical estimation problems aiming to the sensor reduction and applied to gene regulatory networks of apoptosis and Lac operon.

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Section: Future Recommendationsmentioning

confidence: 99%

Maximum-Likelihood State Estimators in Probabilistic Boolean Control Networks

Toyoda

2023

IEEE Trans. Cybern.

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“…Therefore, it is worthily and necessary to deal with the stability analysis and synchronization design issues of MNNs with partly unknown transition rates. So for, it has been found that MNNs apply to many practical systems and any important conclusions with respect to MNNs, which also have been analyzed in the literature such as stability analysis, control design, reachable set estimation problems [5][6][7], other results in [8,9] and the reference therein.…”

Section: Introductionmentioning

confidence: 99%

Event-based finite-time quantized synchronization for discrete Markovian neural networks

Liu

Shi

et al. 2022

Preprint

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This paper is investigated with the robust finite-time synchronization of discrete-time Markovian jump neural networks with partially unknown transition probabilities. Firstly, a unified framework based on event-triggering approaches and quantization, a synchronization error model is first provided for discrete-time Markovian neural networks. Secondly, with the aid of Lyapunov-Krasovskii functional, sufficient conditions for the finite-time boundedness are deduced which can ensure synchronization of the system model, and the quantized finite-time controller and event-triggered parameters are co-designed so that Markovian neural networks are finite-time bounded by variable decoupled techniques. Finally, a simulation example is established to illustrate the availability of the presented methods.MSC Classification: 15A39 , 24D06 , 93B51 , 93D30 , 93E15

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“…, a n ). Recently, many important properties of BNs have been issued [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18], and so on.…”

Section: Introductionmentioning

confidence: 99%

Structural Controllability of Boolean Control Networks under Partial Information

et al. 2021

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It is worth noting that both nodes’ coupling connections and logical updating functions play a vital role in state evolutions of Boolean networks (BNs). In this paper, a new concept named structural controllability (SC) about Boolean control networks (BCNs) with known partial information on nodes’ connections is studied. Then, by referring to semi-tensor product (STP) techniques, several types of SC are presented according to different issues of Boolean functions. Thereafter, several necessary and sufficient conditions are derived for SC of BCNs. Finally, a biological model of the lactose operon in Escherichia coli is simulated to show the effectiveness of the main theoretical results.

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Induced-Equations-Based Stability Analysis and Stabilization of Markovian Jump Boolean Networks

Cited by 39 publications

References 33 publications

Maximum-Likelihood State Estimators in Probabilistic Boolean Control Networks

Maximum-Likelihood State Estimators in Probabilistic Boolean Control Networks

Event-based finite-time quantized synchronization for discrete Markovian neural networks

Structural Controllability of Boolean Control Networks under Partial Information

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