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.
Abstract:
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.
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