ing at the optimization design of Raman fiber amplifier with multiple backward pumps, we combine the artificial bee colony algorithm with the average power analysis to optimize the configuration of backward pump of Raman fiber amplifier. On the basis of the given gain, the optimized model is established in order to minimize the ripple of Raman gain. The average power analysis is utilized to solve Raman scattering equations, and the artificial bee colony algorithm is employed to find the optimal pump wavelength and pump power. The proposed method is used to design the Raman fiber amplifier at C band and C+L band. The results show that the gain ripple can be controlled below ±0.4 dB under different net gain levels. Compared with the existing methods, the combination of artificial bee colony algorithm and average power analysis has a good performance and can obtain the flatter optimization results, which has certain practical value.
.ing to solve the problem that the complex background pixels affect the hyperspectral classification accuracy, the object detection theory is introduced into the hyperspectral image classification domain, and a hyperspectral image classification method based on spectral-spatial feature iteration is proposed. A multi-target constrained classifier (MTCC) is designed by constrained energy minimization method. Based on the detection theory, the MTCC can effectively decrease the influence of complex background data on the classification accuracy. At the same time, to eliminate the over-classification problem caused by the spectral features, the method uses the feedback fusion of spectral-spatial to strengthen the spatial enhancement information so as to improve the classification accuracy gradually. The results of the experiments on the data sets of Purdue, Salinas and Pavia show that the average accuracies of the proposed methods are 98.09%, 97.33% and 84.68% respectively, and the precisions of the proposed method are 96.84%, 95.32% and 79.13% respectively. Compared to other algorithms, the proposed method has higher generalization ability and practicability.
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