支持向量机用于宏纤维复合材料功能退化预测

Qing-Fang Duan, Wei Wang, Zhiguo Yang
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引用次数: 0

摘要

宏观纤维复合材料(MFC)在应力场的作用下会发生功能退化,这是其应用的关键问题之一。为了防止MFC作动器对结构的振动控制效果恶化,有必要提出一种有效的方法来预测MFC的功能退化。本研究将支持向量机(SVM)用于MFC的功能退化预测建模。针对典型的MFC应力函数退化问题,建立了基于支持向量机的非线性预测模型。以实验数据为样本对SVM模型进行训练,提出了一种基于所建立模型的MFC功能退化预测的有效方法。模型对实验数据的拟合误差小于1%,对30MPa应力下MFC功能退化曲线的最大预测误差为6.2%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Support Vector Machine for Function Degradation Prediction of Macro Fiber Composite
Macro fiber composite (MFC) will experience function degradation under the influence of the stress field, which is one of the key concerns in its application. In order to prevent the deterioration of the vibration control effect of the MFC actuator on the structure, it is necessary to propose an effective method to predict the function degradation of MFC. In this study support vector machine (SVM) is adopted in the function degradation prediction modeling of MFC. Aiming at the typical problem of stress-induced function degradation of MFC, a nonlinear prediction model based on SVM is established. The experimental data is used as samples to train the SVM model, and an effective method based on the established model for the function degradation prediction of MFC is proposed. The fitting error of the model to the experimental data is less than 1%, and the maximum prediction error for the MFC function degradation curve under 30MPa stress is 6.2%.
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