Hybrid algorithm for predicting the temperature-variation-induced wavelength drift of DBR semiconductor lasers.

Abstract

In this paper, a hybrid algorithm to predict the wavelength drift induced by ambient temperature variation in distributed Bragg reflector semiconductor lasers is proposed. This algorithm combines the global search capability of a genetic algorithm (GA) and the supermapping ability of an extreme learning machine (ELM), which not only avoids the randomness of ELM but also improves its generalization performance. In addition, a tenfold cross-validation method is employed to determine the optimal activation function and the number of hidden layer nodes for ELM to construct the most suitable model. After applying multiple sets of test data, the results demonstrate that GA-ELM can quickly and accurately predict the wavelength drift, with an average rms error of 4.09×10-4nm and average mean absolute percentage error of 0.21 %. This model is expected to combine the temperature and current tuning models for a wavelength in follow-up research to achieve rapid tuning and high stability of a wavelength without additional devices.