Reservoir Computing (RC), an evolution from Recurrent Neural Networks (RNN), not only represents a unique machine learning paradigm, but also serves as a neuromorphic framework that mirrors the intricate cortical circuits of the human brain. This paper proposes another new photonic RC system based on four basic photonic reservoir computing architectures (single photonic RC system, the parallel photonic RC system, the dual-feedback loop-based photonic RC system and the mutually coupled photonic RC system). System proposed uses optical injection for signal input and retains two parallel responsive semiconductor lasers (-SLs) with self-feedback loops. Meanwhile, two relatively independent -SLs are mutually coupled via two coupling lines. The new photonic RC system adds only two sections of fiber compared to the parallel photonic RC system and the mutually coupled photonic RC system. The experiments show that the system proposed has significant advantages on the nonlinear auto regressive moving average series tasks, the chaotic time series prediction tasks and the waveform classification task. More importantly, the memory capacity of system proposed can be adjust by controlling the delay time of the self-feedback loops, so it has higher memory capacity to handle the higher order nonlinear auto regressive moving average tasks (NARMA20 and NARMA30) after optimizing the parameters.

中文翻译：

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基于光反馈互延迟耦合半导体激光器的储层计算系统

储层计算（RC）是循环神经网络（RNN）的演变，不仅代表了一种独特的机器学习范式，而且还可以作为反映人脑复杂皮质回路的神经形态框架。本文提出了另一种基于四种基本光子库计算架构（单光子RC系统、并行光子RC系统、基于双反馈环路的光子RC系统和互耦合光子RC系统）的新型光子RC系统。所提出的系统使用光注入进行信号输入，并保留两个具有自反馈环路的并行响应半导体激光器（-SL）。同时，两个相对独立的-SL通过两条耦合线相互耦合。与并联光子RC系统和互耦合光子RC系统相比，新型光子RC系统仅增加两段光纤。实验表明，所提出的系统在非线性自回归移动平均序列任务、混沌时间序列预测任务和波形分类任务上具有显着的优势。更重要的是，所提出的系统的内存容量可以通过控制自反馈环路的延迟时间来调整，因此在优化参数后具有更高的内存容量来处理高阶非线性自回归移动平均任务（NARMA20和NARMA30） 。