Enhanced prediction performance of a time-delay reservoir computing system based on a VCSEL by dual-training method.

In this paper, a new dual-training method for a time-delay reservoir computing (RC) system based on a single vertical-cavity surface-emitting laser (VCSEL) is proposed and demonstrated experimentally for the first time. The prediction performance of the RC system by using the dual-training method has been experimentally and numerically investigated. Here, the dual-training method is defined as performing a further RC based on the difference between the target value and the predicted value of the traditional single training. It is found that enhanced prediction performance of the RC system can be obtained by employing the dual-training method, compared to the traditional single training method. More specifically, the NMSE values of the RC system with the dual-training method applied can be improved to 760% compared with the single training method in experiments. Besides, the effects of injection power, bias currents, feedback strength, and frequency detuning are also considered. The proposed dual-training method is of great significance to the performance enhancement of the RC and has an important promotion effect on the application of the RC in the future.

Four-channels reservoir computing based on polarization dynamics in mutually coupled VCSELs system.

A novel Four-channels reservoir computing (RC) based on polarization dynamics in mutually coupled vertical cavity surface emitting lasers (MDC-VCSELs) is proposed and demonstrated numerically. Here, the four channels are realized in two orthogonal polarization modes (x-polarization and y-polarization modes) of two VCSELs for the first time. A chaotic time series prediction task is employed to quantitatively evaluated the prediction performance of the proposed system. It is found that the Four-channels RC based on MDC-VCSELs can produce comparable prediction performance with One-channel RC, and it is possible to increase four times information processing rate by using the Four-channels RC. Besides, the effects of injection current, external injection strength, frequency detuning, coupling strength, as well as internal parameters on the prediction performance of the Four-channels RC based on MDC-VCSELs are carefully examined. Moreover, the influences of sampled period of input signal and the number of virtual nodes are also considered. The proposed Four-channels RC based on MDC-VCSELs is valuable for further enhancing the information processing rate of RC-based neuromorphic photonic systems.

Amorphous silicon with high specific surface area prepared by a sodiothermic reduction method for supercapacitors.

A sodiothermic reduction method has been developed for the preparation of porous silicon using aluminosilicate zeolite NaY as a precursor. The porous silicon with a specific surface area of approximately 570 m(2) g(-1) shows distinct capacitive behavior when used as an electrode material for supercapacitors.