Energy Attenuation Prediction of Dye-Doped PMMA Microfibers by Backpropagation Neural Network - Hang Yu; Juan Liu; Jinjin Han; Minghui Chen; Mingjun Ke; Zhili Lin; Zhijun Wu; Jixiong Pu; Xining Zhang; Hao Dai (IEEE Photonics Journal)
To figure out the energy attenuation of micro/nanofibers (MNFs) more flexibly and conveniently, a backpropagation neural network (BPNN) is proposed to forecast the output intensity of rhodamine B (RhB) doped polymer microfibers (PMFs). According to the diameter, doping concentration, and propagation distance (L), we realize the L-dependence of output energy predictions for the excitation light (IE) and fluorescence (IF) of the doped PMFs. Hundreds of propagation distance-intensity data pairs acquired from dozens of RhB doped PMFs are used for the BPNN training. The prediction ability of the model is evaluated by the root-mean-square error (RMSE), the mean absolute percentage error (MAPE), and R2. The output intensity prediction performance of BPNN is compared with the traditional exponential-fitting (EF) method. The prediction results indicate that the two-hidden-layer network with one and seventeen neurons respectively provides the best performance. After training, BPNN gives a good intensity prediction for both the IE (RMSE = 3.16×10−2, MAPE = 7.3%, and R2 = 0.9802) and the IF (RMSE = 0.91×10−2, MAPE = 0.89%, and R2 = 0.9696) from the output end of the PMF with different diameters and doping concentrations. The energy losses of the two kinds of light from different doped PMFs are also calculated based on the predicted values, which are similar to the ones obtained from the EF method. The approach based on the BPNN prediction for the energy attenuation of the PMFs shows superiority in flexibility and applicability toward the traditional methods, which could promote the optimal design of the MNF devices and the practical application.