Performance optimization of a reservoir computing system based on a solitary semiconductor laser under electrical-message injection

A simple reservoir computing (RC) system based on a solitary semiconductor laser under an electrical message injection is proposed, and the performances of the RC are numerically investigated. Considering the lack of memory capacity (MC) in such a system, some auxiliary methods are introduced to enhance the MC and optimize the performances for processing complex tasks. In the pre-existing method, the input information is the current input data combined with some past input data in a weighted sum in the input layer (named as M -input). Another auxiliary method (named as M -output) is proposed to introduce the output layer for optimizing the performances of the RC system. The simulated results demonstrate that the MC of the system can be improved after adopting the auxiliary methods, and the effectiveness under adopting the M -input integrated with the M -output (named as M -both) is the most significant. Furthermore, we analyze the system performances for processing the Santa Fe time series prediction task and the nonlinear channel equalization (NCE) task after adopting the above three auxiliary methods. Results show that the M -input is the most suitable for the prediction task while the M -both is the most appropriate for the NCE task.