New model for system behavior prediction based on belief rule based systems

Zhou, Zhijie; Hu, Changhua; Xu, Dong-Ling; Yang, Jian‐Bo; Zhou, Donghua

doi:10.1016/j.ins.2010.08.016

Cited by 50 publications

(11 citation statements)

References 53 publications

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“…Assume that x (t) denotes the security situation which is a hidden behavior at time instant t. Then the relationship between the time instant t and t + 1 of the hidden behavior can be described by the following belief rule [21,22]: j,k (j = 1, ..., N, k = 1, ..., N) denotes the belief degree assigned to D j , ˇD ,k denotes the remaining belief degree which is unassigned to any consequent. Â k denotes the rule weight of the kth rule, and ı denotes the weight of the attribute.…”

Section: The Hidden Brb Model For Predicting the Network Security Sitmentioning

confidence: 99%

A method for predicting the network security situation based on hidden BRB model and revised CMA-ES algorithm

Zhou

Zhang

et al. 2016

Applied Soft Computing

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Section: The Hidden Brb Model For Predicting the Network Security Sitmentioning

confidence: 99%

A method for predicting the network security situation based on hidden BRB model and revised CMA-ES algorithm

Zhou

Zhang

et al. 2016

Applied Soft Computing

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“…The above forecasting model as described in (2) has been proposed in [38]. When the forecasting model is constructed, the qualitative knowledge or historical data is mainly used.…”

Section: B Problem Formulation For Predicting the Hidden Behaviormentioning

confidence: 99%

“…When the forecasting model is constructed, the qualitative knowledge or historical data is mainly used. In order to predict the observable behavior accurately, a parameter estimation algorithm for training the parameters of the forecasting model by using the quantitative data or qualitative knowledge has been developed further [38].…”

Section: B Problem Formulation For Predicting the Hidden Behaviormentioning

confidence: 99%

See 1 more Smart Citation

Hidden Behavior Prediction of Complex Systems Based on Hybrid Information

Zhou

Zhang

et al. 2013

IEEE Trans. Cybern.

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It is important to predict both observable and hidden behaviors in complex engineering systems. However, compared with observable behavior, it is often difficult to establish a forecasting model for hidden behavior. The existing methods for predicting the hidden behavior cannot effectively and simultaneously use the hybrid information with uncertainties that include qualitative knowledge and quantitative data. Although belief rule base (BRB) has been employed to predict the observable behavior using the hybrid information with uncertainties, it is still not applicable to predict the hidden behavior directly. As such, in this paper, a new BRB-based model is proposed to predict the hidden behavior. In the proposed BRB-based model, the initial values of parameters are usually given by experts, thus some of them may not be accurate, which can lead to inaccurate prediction results. In order to solve the problem, a parameter estimation algorithm for training the parameters of the forecasting model is further proposed on the basis of maximum likelihood algorithm. Using the hybrid information with uncertainties, the proposed model can combine together with the parameter estimation algorithm and improve the forecasting precision in an integrated and effective manner. A case study is conducted to demonstrate the capability and potential applications of the proposed forecasting model with the parameter estimation algorithm.

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“…Compared with traditional rule based systems, BRB systems provide a more informative knowledge representation scheme for both quantitative data and qualitative information with uncertainties, and it is also capable of approximating complicated nonlinear causal relationships between antecedent inputs and output [45]. In recent years, it has been successfully applied in various areas, such as fault diagnosis, system identification, forecasting and decision analysis [45,46,42,3,50,6,48,25].…”

Section: Introductionmentioning

confidence: 99%