Intermittent dynamics identification and prediction from experimental data of discrete-mode semiconductor lasers by reservoir computing

Abstract

Analysis of intermittent dynamics from experimental data is essential to promote the understanding of practical complex nonlinear systems and their underlying physical mechanisms. In this paper, reservoir computing enabled dynamics prediction, and identification of two types of intermittent switching using experimental data from discrete-mode semiconductor lasers are rigorously studied and demonstrated. The results show that, for the dynamics prediction task, both regular and irregular intermittent switching can be predicted reliably by reservoir computing, achieving the average normalized mean-square error of less than 0.015. Additionally, the impact of the number of virtual nodes in the reservoir layer, as well as the train-test split ratio on prediction performance, is explored. For the dynamic identification task, a 2-class classification test is adopted, and the corresponding binary accuracy is calculated to evaluate the identification performance. The results demonstrate that the accuracy of identifying both regular and irregular intermittent switching exceeds 0.996. Compared with the conventional amplitude threshold identification method, the reservoir computing-driven dynamics identification method exhibits superior accuracy, especially in the intermittent transient transition regions.