A Model for Hidden Behavior Prediction of Complex Systems Based on Belief Rule Base and Power Set

Zhou, Zhijie; Hu, Guanyu; Zhang, Bangcheng; Hu, Changhua; Zhou, Zhiguo; Pei-li, Qiao

doi:10.1109/tsmc.2017.2665880

Cited by 82 publications

(37 citation statements)

References 17 publications

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“…The RIMER method is at the core of a BRB system [9] , and consists of three main steps [10] : (1) calculate the activation weight; (2) amend with the belief degree; and (3) use an Evidence Reasoning (ER) algorithm to synthesize activation rules [11] .…”

Section: Belief Rule-base and Rimer Methodsmentioning

confidence: 99%

Selective ensemble learning method for belief-rule-base classification system based on PAES

Liu

Weikun

Wang

et al. 2019

Big Data Min. Anal.

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Traditional Belief-Rule-Based (BRB) ensemble learning methods integrate all of the trained sub-BRB systems to obtain better results than a single belief-rule-based system. However, as the number of BRB systems participating in ensemble learning increases, a large amount of redundant sub-BRB systems are generated because of the diminishing difference between subsystems. This drastically decreases the prediction speed and increases the storage requirements for BRB systems. In order to solve these problems, this paper proposes BRBCS-PAES: a selective ensemble learning approach for BRB Classification Systems (BRBCS) based on Pareto-Archived Evolutionary Strategy (PAES) multi-objective optimization. This system employs the improved Bagging algorithm to train the base classifier. For the purpose of increasing the degree of difference in the integration of the base classifier, the training set is constructed by the repeated sampling of data. In the base classifier selection stage, the trained base classifier is binary coded, and the number of base classifiers participating in integration and generalization error of the base classifier is used as the objective function for multi-objective optimization. Finally, the elite retention strategy and the adaptive mesh algorithm are adopted to produce the PAES optimal solution set. Three experimental studies on classification problems are performed to verify the effectiveness of the proposed method. The comparison results demonstrate that the proposed method can effectively reduce the number of base classifiers participating in the integration and improve the accuracy of BRBCS.

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Section: Belief Rule-base and Rimer Methodsmentioning

confidence: 99%

Selective ensemble learning method for belief-rule-base classification system based on PAES

Liu

Weikun

Wang

et al. 2019

Big Data Min. Anal.

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“…Since the set of all subsets, i.e. the powerset of M 1-hop has 2 N1-hop elements [31], the number of k-subsets of M 1-hop for k ∈ {1 .. N 1-hop − 1} grows exponentially with the number of 1-hop neighbours of the joining node. Furthermore, the computation of P nc in (10) also involves nested iteration over all time slots in the frame and all nodes within a k-subset of M 1-hop .…”

Section: B Estimating the Number Of Collisionsmentioning

confidence: 99%

Scalable Adaptive Networking for the Internet of Underwater Things

Morozs

Mitchell

Diamant

2020

IEEE Internet Things J.

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Internet of Underwater Things (IoUT) systems comprising tens or hundreds of underwater acoustic communication nodes will become feasible in the near future. The development of scalable networking protocols is a key enabling technology for such IoUT systems, but this task is challenging due to the fundamental limitations of the underwater acoustic communication channel: extremely slow propagation and limited bandwidth. The aim of this paper is to propose the JOIN protocol to enable the integration of new nodes into an existing IoUT network without the control overhead of typical state-of-the-art solutions. The proposed solution is based on the capability of a joining node to incorporate local topology and schedule information into a probabilistic model that allows it to choose when to join the network to minimize the expected number of collisions. The proposed approach is tested in numerical simulations and validated in two sea trials. The simulations show that the JOIN protocol achieves fast convergence to a collision-free solution, fast network adaptation to new nodes, and negligible network disruption due to collisions caused by a joining node. The sea trials demonstrate the practical feasibility of this protocol in real UAN deployments and provide valuable insight for future work on the trade-off between control overhead and reliability of the JOIN protocol in a harsh acoustic communication environment.

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“…Therefore, to solve the problem where the existing methods only consider the limitation of a single knowledge, fuzzy, uncertain information, etc., Yang et al described the belief rule base modeling concept [22] based on the evidence reasoning method [23] on the basis of the D-S theory [24], fuzzy theory [25] and the traditional IF-THEN rule base, where the confidence level is introduced and combined with expert knowledge to reflect the behavior of the complex system. A rule base [26], [27] containing all the confidence levels of the results can well deal with the problem of the health status prediction of the complex system [28]. To this end, this paper proposes a BRB-prediction model based on the health status for a high-speed train running gear system, which uses semiquantitative information to establish a health status prediction model for the system.…”

Section: Introductionmentioning

confidence: 99%

Health Status Prediction Based on Belief Rule Base for High-Speed Train Running Gear System

et al. 2019

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The running gear is a vital component of a high-speed train to ensure operation safety. Accurately predicting the future health status of the running gear is significant to keep the reliability and safety of trains. It is difficult to predict the future health status based on the analytical model of the running gear system because of its complexity and coupling. Moreover, the fault data are a minor part of tremendous data in the running and monitoring process of a high-speed train, which obstructs accurately predicting the health status based on a data-driven method. To solve the above problems, this paper proposes a health status prediction method based on the belief rule base (BRB) for the running gear system. First, a failure mechanism is analyzed to confirm the fault characteristics, which can represent the health status of the running gear system. Second, in order to avoid the limitations of a single sensor acquisition, such as a lack of comprehensiveness and robustness, singular value decomposition is used to achieve multisensory information fusion. The fused features are used as the input to the health status prediction model. Data fusion is a way to improve the precision of the health status prediction in the model input. Then, this model based on the BRB is established using the fault data and expert knowledge. During the process of prediction, the subjectivity of experts makes the initial BRB imprecise, so a projection constrained covariance matrix adaptive evolution strategy algorithm is needed to optimize the initial parameters and improve the accuracy of the proposed model. Finally, a case study for the running gear system is carried out to verify the effectiveness and accuracy of the proposed model. The results show that the proposed model can help to accurately predict the health status of the running gear system. INDEX TERMS Belief rule base, projection constrained covariance matrix adaptive evolution strategy, fatal degree, singular value decomposition, health status prediction.

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A Model for Hidden Behavior Prediction of Complex Systems Based on Belief Rule Base and Power Set

Cited by 82 publications

References 17 publications

Selective ensemble learning method for belief-rule-base classification system based on PAES

Selective ensemble learning method for belief-rule-base classification system based on PAES

Scalable Adaptive Networking for the Internet of Underwater Things

Health Status Prediction Based on Belief Rule Base for High-Speed Train Running Gear System

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