Jin Jiaoyang, Jiang Ning, Zhang Yiqun, Feng Weizhou, Zhao Anke, Liu Shiqin, Peng Jiafa, Qiu Kun, Zhang Qianwu
Opt Express. 2022 Apr 11;30(8):13647-13658. doi: 10.1364/OE.454852.
We propose an adaptive time-delayed photonic reservoir computing (RC) structure by utilizing the Kalman filter (KF) algorithm as training approach. Two benchmark tasks, namely the Santa Fe time-series prediction and the nonlinear channel equalization, are adopted to evaluate the performance of the proposed RC structure. The simulation results indicate that with the contribution of adaptive KF training, the prediction and equalization performance for the benchmark tasks can be significantly enhanced, with respect to the conventional RC using a training approach based on the least-squares (LS). Moreover, by introducing a complex mask derived from a bandwidth and complexity enhanced chaotic signal into the proposed RC, the performance of prediction and equalization can be further improved. In addition, it is demonstrated that the proposed RC system can provide a better equalization performance for the parameter-variant wireless channel equalization task, compared with the conventional RC based on LS training. The work presents a potential way to realize adaptive photonic computing.
我们提出一种自适应时延光子储层计算（RC）结构，通过利用卡尔曼滤波器（KF）算法作为训练方法。采用两个基准任务，即圣达菲时间序列预测和非线性信道均衡，来评估所提出的RC结构的性能。仿真结果表明，在自适应KF训练的作用下，相对于使用基于最小二乘法（LS）训练方法的传统RC，基准任务的预测和均衡性能可得到显著提高。此外，通过将从带宽和复杂度增强的混沌信号导出的复掩码引入所提出的RC中，预测和均衡性能可进一步提高。另外，结果表明，与基于LS训练的传统RC相比，所提出的RC系统可为参数变化的无线信道均衡任务提供更好的均衡性能。这项工作提出了一种实现自适应光子计算的潜在方法。
