一种RVM模糊模型辨识方法及在故障预报中的应用  

An RVM Fuzzy Model Identification Method and Its Application to Fault Prediction

作　　者：胡昌华[1] 王兆强[1] 周志杰[1] 司小胜[1,2] 

机构地区：[1]第二炮兵工程学院302教研室,西安710025 [2]清华大学自动化系,北京100084

出　　处：《自动化学报》2011年第4期503-512,共10页Acta Automatica Sinica

基　　金：国家自然科学基金(60736026;61004069);国家杰出青年基金(61025014)资助~~

摘　　要：对复杂、病态、非线性动态系统进行故障预报的重点和难点是建立系统故障状况的数学模型,通常难以建立精确的数学模型,相比之下构建其模糊模型是一个有效途径.本文研究了相关向量机(Relevance vector machine,RVM)与模糊推理系统(Fuzzy inference system,FIS)之间的内在联系,证明了基于RVM的FIS具有一致逼近性,并提出了一种基于RVM和梯度下降(Gradient descent,GD)算法的模糊模型辨识方法.基于所给出的模糊模型辨识方法提出了一种新的故障预报算法.仿真结果表明所建立的模糊模型不仅结构更加简单,而且能达到更高的预测精度,所提出的故障预报算法能准确地预报系统故障.For a dynamic system with complexity, morbidity and nonlinearity, it is significant and difficult to establish a fault prediction model accurately in general. Instead, to construct a suitable fuzzy model may be an effective alternative. In this paper, the inherent relationship between relevance vector machine （RVM） and fuzzy inference system （FIS） is investigated firstly, then the uniformly approximating capability of FIS based on RVM is proved. Next, a fuzzy model identification method based on RVM and gradient descent （GD） algorithm is presented as well. Finally, a new fault prediction algorithm is given on the basis of the presented fuzzy model identification method. The simulation studies illustrate that the presented fuzzy modeling method can generate a compacter model and achieve higher prediction accuracy as well. Based on the new fault prediction algorithm, the system fault can be predicted correctly.

关 键 词：故障预报 模糊模型 系统辨识 相关向量机 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]