1. Reservoir computing system based on mutually delay-coupled semiconductor lasers with optical feedback.
- Author
-
You, Meiming, Yang, Xuesong, Jiang, Dongchen, and Wang, Guoqiang
- Subjects
- *
OPTICAL feedback , *COMPUTER systems , *RECURRENT neural networks , *TIME delay systems , *SEMICONDUCTOR lasers , *SHORT-term memory , *PHOTONIC crystal fibers - 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. • The proposed system integrates the advantages of the dual-feedback loop-based and mutually coupled photonic RC systems. • The proposed system can adjust its memory capacity to suit various short-term memory needs. • The inclusion of mutual coupling and self-feedback structures in the proposed system enriches its dynamic characterization. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF