Fault detection for probabilistic boolean networks

Leifeld, Thomas; Zhang, Zhihua; Zhang, Ping

doi:10.1109/ecc.2016.7810377

Cited by 13 publications

(11 citation statements)

References 29 publications

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“…In light of (20), the recursion result (10) for P f k (i k ) and P f k+1 (i k+1 ) can be finally converted into the following recursion for the normalized variables:…”

Section: The Above Equation Yieldsmentioning

confidence: 99%

“…Therefore, the logarithm of Pr {y0:K |θ, u0:K−1} is utilized to cope with the model evaluation problem, which can be calculated from the scaling factors as log Pr {y0:K |θ, u0:K−1} = K λ=0 log ξ λ . Remark 11: In the interesting paper [20], an observer has been designed to evaluate the probabilistic distribution of the state vectors. Based on this observer, a novel fault detection scheme has been proposed for the mix-valued probabilistic BNs.…”

Section: The Above Equation Yieldsmentioning

confidence: 99%

“…Based on this observer, a novel fault detection scheme has been proposed for the mix-valued probabilistic BNs. It is worth pointing out that the algorithm proposed in [20] cannot be further utilized to cope with the fault diagnosis problems. If all the possible types of system faults are known which are modeled respectively by θ1, θ2, .…”

Section: The Above Equation Yieldsmentioning

confidence: 99%

“…Recently, the fault detection and diagnosis problem for BNs has been investigated based on the STP technique and many novel results have been presented in the literature, see e.g. [14], [20].…”

Section: Introductionmentioning

confidence: 99%

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Model Evaluation of the Stochastic Boolean Control Networks

Chen

Wang

Shen

et al. 2022

IEEE Trans. Automat. Contr.

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This paper investigates the model evaluation problem for the stochastic Boolean control networks (SBCNs). First, an algebraic expression of the SBCN is obtained based on the semi-tensor product method, and a straightforward approach is then proposed to compute the probability that the given observed output sequence is produced by the considered model. Second, two recursive algorithms, namely the forward and the backward algorithms, are designed for model evaluation by resorting to the forward-backward technique. In addition, scaling factors are introduced to deal with the numerical issues arising in the implementation of the developed algorithms. Finally, to illustrate the applicability and effectiveness of the proposed algorithms, a Boolean model of the lac operon is employed as an example for numerical simulation.

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“…In light of (20), the recursion result (10) for P f k (i k ) and P f k+1 (i k+1 ) can be finally converted into the following recursion for the normalized variables:…”

Section: The Above Equation Yieldsmentioning

confidence: 99%

Section: The Above Equation Yieldsmentioning

confidence: 99%

Section: The Above Equation Yieldsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Model Evaluation of the Stochastic Boolean Control Networks

Chen

Wang

Shen

et al. 2022

IEEE Trans. Automat. Contr.

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“…PBNs with the addition of Boolean control inputs are called probabilistic Boolean control networks (PBCNs). Since the introduction of semi-tensor product (STP) of matrices by Cheng et al [3], many fundamental properties of switched Boolean control networks, PBNs and PBCNs have been characterized in the literature including but not limited to observability [4]- [6], controllability [7]- [9], reconstructibility [4], fault detection [10], stabilizability [11]- [15], structure identification [16], [17], output tracking control [18], [19] and model checking [20].…”

Section: Introductionmentioning

confidence: 99%

Optimal Control of Probabilistic Boolean Networks: An Information-Theoretic Approach

Sonam¹,

Sutavani

Wagh³

et al. 2021

IEEE Access

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Semi‐tensor product‐based algebra‐logic mixed representation and fault diagnosis for a class of gate networks

Liu

Shen

Moschoyiannis

et al. 2023

Asian Journal of Control

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This article studies an algebra‐logic mixed representation of gate networks and its application to stuck‐at fault diagnosis. First, the gate network is characterized through a logic expression of disjoint sum‐of‐products, and the system structure of the gate network is described based on 2‐to‐1 multiplexers. Then, by resorting to the semi‐tensor product of matrices, a novel algebra‐logic mixed representation is proposed for the gate network through its logic expression and system structure. Furthermore, a novel stuck‐at fault diagnosis algorithm for the gate network is presented, where the stuck‐at fault testability of the gate network is equivalent to the solution existence of the system of linear equations. Finally, the fault diagnosis of the 4‐bit carry look‐ahead adder is carried out to demonstrate the effectiveness and feasibility of the proposed theoretical approach and algorithms.

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Fault detection for probabilistic boolean networks

Cited by 13 publications

References 29 publications

Model Evaluation of the Stochastic Boolean Control Networks

Model Evaluation of the Stochastic Boolean Control Networks

Optimal Control of Probabilistic Boolean Networks: An Information-Theoretic Approach

Semi‐tensor product‐based algebra‐logic mixed representation and fault diagnosis for a class of gate networks

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