【摘要】 针对光栅传感器在高温下输出呈现非线性变化的问题，建立了融入ELM极限学习机的非线性模型，应用建立的模型根据温度补偿光栅的输出信号预测实验温度，并进一步得出相应温度下压力敏感光栅的输出，解决光栅压力传感器高温下的温度补偿问题，实验结果证明该模型可较好解决高温Bragg压力传感器的温度补偿问题，ELM极限学习机的预测与实际结果偏差较小，检测误差最大仅1.231%FS。更多还原

【Abstract】 In response to the issue of non-linear changes in the output of grating sensors at high temperatures, a nonlinear model integrated with ELM extreme learning machine is established, the experimental temperature is predicted through temperature compensation grating, and the output of pressure-sensitive grating at the corresponding temperature is predicted, so as to solve the temperature compensation problem of grating pressure sensor at high temperature. The experimental results show that the model can effectively solve the temperature compensation of Bragg pressure sensor at high temperature. The prediction of the Extreme Learning Machine(ELM)has a small deviation from the actual results, with a maximum detection error of only 1.231%FS.更多还原