Appl Opt. 2022 Apr 20;61(12):3473-3479. doi: 10.1364/AO.454422.
Time-delayed reservoir computing (RC) is a brain inspired paradigm for processing temporal information, with simplification in the network's architecture using virtual nodes embedded in a temporal delay line. In this work, a novel, to the best of our knowledge, RC system based on a dual-loop optoelectronic oscillator is proposed to enhance the prediction and classification. The hardware is compact and easy to implement, and only a section of fiber compared to the traditional optoelectronic oscillator reservoir is added to conform the dual-loop scheme. Compared with the traditional reservoir, a remarkable performance of the proposed RC system is demonstrated by simulation on three well-known tasks, namely the nonlinear auto regressive moving average (NARMA10) task, signal waveform recognized task, and handwritten numeral recognition. The parameter optimization in the NARMA10 task is presented with influenced factors. The novel RC system finally obtains a normalized mean square error at 0.0493±0.007 in NARMA10 task, 6.172×10 in signal waveform recognized task, and a word error rate at 9% in handwritten numeral recognition with suitable parameters.
时滞 reservoir computing (RC) 是一种受大脑启发的处理时间信息的范例，通过在时间延迟线上嵌入虚拟节点来简化网络架构。在这项工作中，提出了一种新颖的、据我们所知的基于双环光电振荡器的 RC 系统，以增强预测和分类能力。该硬件结构紧凑，易于实现，与传统的光电振荡器 reservoir 相比，仅需添加一小段光纤即可构成双环方案。与传统 reservoir 相比，通过对三个著名任务（即非线性自回归移动平均 (NARMA10) 任务、信号波形识别任务和手写数字识别任务）的仿真，展示了所提出的 RC 系统的显著性能。在 NARMA10 任务中提出了参数优化，并给出了受影响因素。对于新型 RC 系统，在适当的参数下，在 NARMA10 任务中归一化均方误差为 0.0493±0.007，在信号波形识别任务中为 6.172×10，在手写数字识别任务中误码率为 9%。
