利用GWO-LSSVM算法对光纤压力传感器进行温度补偿

Temperature compensation of optical fiber pressure sensor based on GWO-LSSVM

光纤压力传感器工作性能受温度影响较大,需进行温度补偿。针对这一问题,提出了灰狼算法与最小二乘支持向量机(GWO-LSSVM)算法相结合的软件补偿方案,利用灰狼算法在指定范围内迭代优化最小二乘支持向量机的惩罚因子ζ和核参数σ以求构建补偿算法模型。在不同温度环境下,对传感器进行标定试验测得传感器的输入输出数据,分成测试集和训练集。以测试集的预测值计算的均方根误差为适应度函数,将温度补偿问题转化为带约束的凸二次优化问题。结果表明,相较于补偿前,温度补偿后的光纤压力传感器的灵敏度温度系数由9.405×10^(-3)/℃提升到1.2016×10^(-4)/℃,温度附加误差相对值由28.215%提升到0.481%,传感器的温度稳定性得到了很大程度的改善。

The optical fiber pressure sensor needs temperature compensation because its performance is greatly affected by temperature.To solve this problem,a software compensation scheme combining grey wolf optimization and least squares support vector machine(GWO-LSSVM)algorithm is proposed.The penalty factorζand kernel parameterσof least squares support vector machine are iteratively optimized by grey wolf optimization algorithm within the specified range to construct the compensation algorithm model.In different temperature situations,the input and output data of the sensor are measured by calibration test and are divided into test set and training set.By taking the root mean square error which is calculated from the predicted values of the test set as the fitness function,the temperature compensation problem is transformed into a convex quadratic optimization problem with constraints.The results show that compared with previous compensation,the sensitivity temperature coefficient of the fiber optic pressure sensor after temperature compensation is increased from 9.405×10^(-3/)℃to 1.2016×10^(-4)/℃,and the relative value of the additional temperature error is increased from 28.215%to 0.481%.The temperature stability of the sensor is greatly improved.