Research on FBG Demodulation Algorithm Based on Improved GRNN Model
Drying temperature monitoring in the wood drying chamber is one of the important fire protection measures in wood processing plants,and the material and sensing properties of the FBG sensor allow it to monitor the internal temperature of the drying chamber without becoming a new fire trigger.In order to solve the shortcomings of low di-rect demodulation accuracy in the wavelength demodulation process of FBG sensor,a demodulation algorithm based on Cauchy distribution curve fitting and SENet improved GRNN in FBG sensor was studied.The Cauchy curve fitting and Hilbert transform were used to segment the peak region of the FBG reflection curve,the peak region data was composed of training samples and test samples for the improved GRNN model,the SENet structure was embedded in-to the GRNN model to improve the performance of the GRNN model,and the optimal smoothing factor for the im-proved GRNN was obtained through the PSO algorithm.The experimental results show that the demodulation temper-ature error is about 0.15℃ and the wavelength error is 1.8pm,compared with the direct demodulation method and the unimproved GRNN model,the performance of the improved GRNN model embedded in the SENET structure is a-bout 76％and 84％,respectively,which effectively improves the demodulation accuracy of the FBG sensor.
万方数据
维普
