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Loads location identification of fiber optic smart structures based on Genetic Algorithm-Support Vector Regression

机译：基于遗传算法 - 支持向量回归的光纤智能结构的位置识别

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摘要

A load location identification method of fiber optic smart structures based on a prototype data acquisition system was proposed. It employed genetic algorithm - support vector regression to estimate X, Y coordinates of loads loading on a composite panel. The panel is embedded with 8 sensors. When loads act on any position of smart composite structures, the output values of embedded fiber near the loading position will change. The 8-changed signals are the features of genetic algorithm - support vector regression. The data acquisition center collects the features as the data samples. Data samples are divided into training set, validation set and testing set. Then support vector regression model was established by using the training set and validation set and the parameters were optimized by genetic algorithm. The study compared the prediction accuracy between genetic algorithm - support vector regression model and back propagation model. The comparison results showed that the prediction accuracy of testing set was 85.78% and it is better than the back propagation neural network prediction model. This paper demonstrates that using genetic algorithm - support vector regression model to identify loads is not only stable and feasible, but also with high precision. The presented method in this paper is significant for the health monitoring and damage identification of composite materials in future.
机译：提出了一种基于原型数据采集系统的光纤智能结构的负载位置识别方法。它采用了遗传算法 - 支持向量回归估计复合面板上的负载X，Y坐标。面板嵌入有8个传感器。当加载在智能复合结构的任何位置采用时，装载位置附近的嵌入光纤的输出值将改变。 8变化的信号是遗传算法的特征 - 支持向量回归。数据采集​​中心收集数据样本的功能。数据样本分为训练集，验证集和测试集。然后通过使用训练集和验证集建立支持向量回归模型，并且通过遗传算法优化参数。该研究比较了遗传算法 - 支持向量回归模型与后传播模型的预测精度。比较结果表明，检测集的预测精度为85.78％，它比背传播神经网络预测模型更好。本文演示了使用遗传算法 - 支持向量回归模型来识别负载不仅是稳定和可行的，而且具有高精度。本文的呈现方法对于未来复合材料的健康监测和损害识别是重要的。

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