Inference analysis and adaptive training for belief rule based systems |
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Authors: | Yu-Wang Chen Jian-Bo Yang Dong-Ling Xu Zhi-Jie Zhou Da-Wei Tang |
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Affiliation: | 1. School of Mechanical Engineering, Institute of Intelligent Manufacturing and Information Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;2. Heudiasyc Laboratory, UMR CNRS 7253, Research Center of Royallieu, Sorbonne Universités, Université de Technologie de Compiègne, Compiègne 60203, France;3. Shanghai Key Lab of Advanced Manufacturing Environment, Shanghai, China |
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Abstract: | 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. |
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