Reservoir computing system based on mutually delay-coupled semiconductor lasers with optical feedback

Abstract

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 (R-SLs) with self-feedback loops. Meanwhile, two relatively independent R-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.