Abstract
Belief rule-based (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 and knowledge with uncertainties can be represented using belief structures, and a belief rule is designed with belief degrees embedded in its possible consequents. In this paper, we first review the scheme of belief rules for representing and inferring uncertain knowledge. Then we present two BRB system identification methods in which different training objectives are used. Finally, numerical studies are conducted to demonstrate the capability of BRB systems on uncertain nonlinear system modeling and identification.
This work was partially supported by the UK Engineering and Physical Science Research Council under Grant No.: EP/F024606/1 and the EC REFERENCE project.
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Chen, YW., Yang, JB., Xu, DL. (2013). Uncertain Nonlinear System Modeling and Identification Using Belief Rule-Based Systems. In: Qin, Z., Huynh, VN. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2013. Lecture Notes in Computer Science(), vol 8032. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39515-4_19
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DOI: https://doi.org/10.1007/978-3-642-39515-4_19
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