Open AccessParallel information processing by a reservoir computing system based on a VCSEL subject to double optical feedback and optical injection
Author(s) -
Xiang-Sheng Tan,
Yuhan Hou,
Zheng-Mao Wu,
Guang-Qiong Xia
Publication year - 2019
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.27.026070
Subject(s) - polarization (electrochemistry) , vertical cavity surface emitting laser , computer science , reservoir computing , signal processing , optics , parallel processing , node (physics) , nonlinear system , optical communication , laser , physics , parallel computing , computer hardware , artificial intelligence , acoustics , digital signal processing , artificial neural network , chemistry , recurrent neural network , quantum mechanics
In this work, we propose a scheme of reservoir computing (RC) for processing a Santa-Fe time series prediction task and a signal classification task in parallel, and the performances of the RC have been numerically investigated. For this scheme, a vertical-cavity surface-emitting laser (VCSEL) simultaneously subject to double optical feedback and optical injection is utilized as a nonlinear node, and the parallel information processing of the RC system is implemented based on the dynamical responses of X polarization component (X-PC) and Y polarization component (Y-PC) in the VCSEL. Considering that two different feedback frames (polarization-preserved optical feedback (PP-OF) or polarization-rotated optical feedback (PR-OF)) may be adopted in two feedback loops, four feedback combination cases are numerically analyzed. The simulated results show that the parallel processing ability of the proposed RC system depends on the feedback frames adopted in two loops. After comprehensively evaluating the parallel processing performances of the two tasks under different feedback combinations, the best parallel processing performance can be achieved by adopting PP-OFs in both two feedback loops. Under some optimized operation parameters, this proposed RC system can realize the lowest prediction error of 0.0289 and the lowest signal classification error of 2.78 × 10 -5 .