We experimentally investigate the performances of an optical reservoir computing (RC) system based on two parallel time-delay reservoirs composed of two semiconductor lasers (SLs) subject to optical feedback. In such a system, the information being processed is split into two parts to send into two reservoirs through directly modulating the pump currents of two SLs, and the temporal output of the two SLs are sampled and taken as the virtual node states for training and testing. Via Santa Fe time series prediction task and multi-waveform recognition task, the performances of the proposed RC system are investigated and compared with those of the system based on one reservoir. The results show that the system based on two parallel reservoirs behaves better performance and stronger parameter robustness than that based on one reservoir. Moreover, through analyzing the dependence of the system performances on the number of virtual node states actually used for readout, the potential data processing rate (DPR) of the system is evaluated. For processing a prediction task under guaranteeing the normalized mean square error below 0.1 and a recognition task under guaranteeing the signal error rate below 0.005, the potential DPR of the proposed RC system can achieve 200 MSa/s, which is twice the DPR of the system with only one reservoir.

中文翻译：

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基于两个并行时滞储层的光储层计算系统实验研究


我们通过实验研究了光学储层计算（RC）系统的性能，该系统基于两个并行时滞储层，该储层由两个受光反馈的半导体激光器（SL）组成。在这样的系统中，通过直接调制两个SL的泵浦电流，将正在处理的信息分成两部分发送到两个存储库，并对两个SL的时间输出进行采样并作为虚拟节点状态进行训练和测试。通过Santa Fe时间序列预测任务和多波形识别任务，研究了所提出的RC系统的性能，并与基于一个水库的系统的性能进行了比较。结果表明，基于两个并行水库的系统比基于一个水库的系统具有更好的性能和更强的参数鲁棒性。此外，通过分析系统性能对实际用于读出的虚拟节点状态数量的依赖性，评估系统的潜在数据处理速率（DPR）。对于在保证归一化均方误差低于 0.1 的情况下处理预测任务和在保证信号错误率低于 0.005 的情况下处理识别任务，所提出的 RC 系统的潜在 DPR 可以达到 200 MSa/s，这是系统 DPR 的两倍只有一个水库。