Identification of uncertain nonlinear systems: Constructing belief rule-based models
Identification of uncertain nonlinear systems: Constructing belief rule-based models
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DOI：
10.1016/j.knosys.2014.09.010
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Yu-wang Chen;Jianbo Yang;Changchun Pan;Dongling Xu;Zhi-Jie Zhou
中科院分区：
文献类型：
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作者：
Yu-wang Chen;Jianbo Yang;Changchun Pan;Dongling Xu;Zhi-Jie Zhou
针对不确定非线性系统的辨识问题，提出了一种基于置信规则的可靠模型。BRB方法是从证据推理（ER）方法和传统的IF-THEN规则为基础的系统。它可以用来建模复杂的非线性因果关系的前因属性和后果在不同类型的不确定性。在BRB模型中，各种类型的信息和知识的不确定性，可以表示使用信念结构，和信念规则的设计与信念度嵌入其可能的后果。在本文中，我们首先介绍了BRB建模不确定非线性系统的方法。然后，我们提出了一个比较分析的三个BRB识别模型，通过结合BRB方法与非线性优化技术。基于l ∞-范数和最小化置信规则中的平均不确定性的BRB辨识模型（MUBR）显示了同时捕获不确定非线性系统区间输出的上下界和下界的能力。简要讨论了辨识精度和区间可信度之间的权衡分析。最后，一个简化的汽车动力学的数值研究进行了证明的能力和有效性的BRB识别模型的建模和识别的不确定非线性系统。
The objective of this paper is to construct reliable belief rule-based (BRB) models for the identification of uncertain nonlinear systems. The BRB methodology is developed from the evidential reasoning (ER) approach and traditional IF–THEN rule based system. It can be used to model complicated nonlinear causal relationships between antecedent attributes and consequents under different types of uncertainty. In a BRB model, 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 introduce the BRB methodology for modelling uncertain nonlinear systems. Then we present a comparative analysis of three BRB identification models through combining the BRB methodology with nonlinear optimisation techniques. The novel BRB identification models usingl∞-norm and minimising mean uncertainties in belief rules (MUBR) show remarkable capabilities of capturing the lower and upper bounds of the interval outputs of uncertain nonlinear systems simultaneously. Trade-off analysis between identification accuracy and interval credibility are briefly discussed. Finally, a numerical study of a simplified car dynamics is conducted to demonstrate the capability and effectiveness of the BRB identification models for the modelling and identification of uncertain nonlinear systems.