Inference analysis and adaptive training for belief rule based systems
Inference analysis and adaptive training for belief rule based systems
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基于信念规则的系统的推理分析和自适应训练
DOI：
10.1016/j.eswa.2011.04.077
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发表时间：
2011-09-15
影响因子：
8.5
通讯作者：
Tanga, Da-Wei
中科院分区：
文献类型：
--
作者：
Chen, Yu-Wang;Yang, Jian-Bo;Tanga, Da-Wei
信念规则库（Belief Rule Base，BRB）系统是传统的基于IF-THEN规则的系统的扩展，能够捕捉前件属性和后件之间复杂的非线性因果关系。在BRB系统中，各种类型的不确定性信息可以用信念结构来表示，并设计了一个信念规则，其中h个信念度嵌入其可能的结果中。对于一组先行属性的输入，BRB中的推理是使用证据推理（ER）方法实现的。本文首先分析了ER算法的推理机制，揭示了其单调推理和非线性逼近模式。对于一个实际的防屈曲支撑系统，仅凭专家的主观知识很难准确地确定其参数。此外，适当调整的参数的BRB系统使用现有的历史数据可以导致显着改善其预测性能。本文提出了一种用于BRB参数更新的训练数据选择方案和自适应训练方法。最后，数值研究的多模态函数和一个实际的管道泄漏检测问题进行说明BRB系统的功能和验证的自适应训练技术的性能。(C)2011爱思唯尔有限公司保留所有权利。
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 h 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. (C) 2011 Elsevier Ltd. All rights reserved.