Inference analysis and adaptive training for belief rule based systems

Yu Wang Chen, Jian Bo Yang, Dong Ling Xu, Zhi Jie Zhou, Da Wei Tang

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Belief rule base (BRB) systems are an extension of traditional IF-THEN rule based systems and capable of capturing complicated nonlinear causal relationships between antecedent attributes and consequents. In a BRB system, various types of information with uncertainties can be represented using belief structures, and a belief rule is designed with belief degrees embedded in its possible consequents. For a set of inputs to antecedent attributes, inference in BRB is implemented using the evidential reasoning (ER) approach. In this paper, the inference mechanism of the ER algorithm is analyzed first and its patterns of monotonic inference and nonlinear approximation are revealed. For a practical BRB system, it is difficult to determine its parameters accurately by using only experts' subjective knowledge. Moreover, the appropriate adjustment of the parameters of a BRB system using available historical data can lead to significant improvement on its prediction performance. In this paper, a training data selection scheme and an adaptive training method are developed for updating BRB parameters. Finally, numerical studies on a multi-modal function and a practical pipeline leak detection problem are conducted to illustrate the functionality of BRB systems and validate the performance of the adaptive training technique. © 2010 Elsevier Ltd. All rights reserved.
Original languageEnglish
Pages (from-to)12845-12860
Number of pages15
JournalExpert Systems with Applications
Issue number10
Publication statusPublished - 15 Sept 2011


  • Adaptive training
  • Belief rule base
  • Evidential reasoning
  • Inference analysis
  • Leak detection
  • Multi-modal function


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