光纤光栅电压传感器非线性校正研究

Research on nonlinear correction of fiber Bragg grating voltage sensor

针对传统的光纤光栅电压传感器非线性校正算法具有运行速度慢,拟合精度不高的缺陷。在研究了大量国内外文献过后,本文为了解决一些传统非线性校正方法在光栅光纤传感器校正中的不足,在此提出了一种基于蚁群算法优化的分段支持向量机回归的校正算法。由于传统的蚁群算法在信号处理中搜索速度不理想,最小二乘支持向量机回归算法精度不高,所以此算法是结合了蚁群算法搜索最小二乘支持向量机回归最佳参数原理的基础上将样本空间按照数据分布情况进行分段回归,以此减少算法运行时间。首先通过蚁群算法优化各个支持向量机参数,然后通过分段回归得到传感器完整的特性,曲线拟合精度为99.97%。此算法克服了传统支持向量机回归算法中局部最优解的问题,具有较好的全局收敛效果。

In order to solve some shortcomings of traditional nonlinear correction methods in grating opti- cal fiber sensor calibration,a segmented correction algorithm is put forward based on support vector regressions （SVR） of ant colony optimization （ACO）. Because the search speed of the traditional ant colo- ny algorithm is not ideal in the signal processing, and the precision of the least squares support vector machine regression algorithm is not high, this algorithm combines the ant colony algorithm to search the optimal parameter principle of the least square support vector machine, and the sample space is piecewise regression according to the data distribution to reduce the running time of the algorithm. The parameters of each SVM can be optimized by ACO, and the consequence is put together to achieve the integrated characteristic of the sensor. The fitness of the algorithm can reach 99.97%. This algorithm overcomes the local optimal solution problem,and has better global convergence effect.