Reservoir Computing Based on Two Parallel Reservoirs Under Identical Electrical Message Injection

Abstract

In this work, we propose and numerically investigate a scheme for reservoir computing (RC) based on two parallel reservoirs under identical electrical message injection, in which two semiconductor lasers (SLs) under optical feedback are utilized as two parallel reservoirs. For simplifying the system, only one mask signal is employed in this scheme. After multiplying with input data, the masked information is injected into two parallel reservoir lasers (SL1 and SL2). The virtual node states can be obtained from the temporal outputs of two SLs. RC can be accomplished by three ways, namely RC1/RC2 (the virtual node states originating from SL1/SL2 are used for training and testing) and RCM (the merged virtual node states originating from two SLs are used for training and testing). Via chaotic time series prediction task and memory capacity (MC) test, the performance of the RC system is simulated and assessed. The results show that, for a given data processing rate, better prediction performance and higher MC can be realized by RCM through setting suitable mismatched parameters between the two SLs. Under satisfying the requirement for achieving a good performance, the highest data processing rate can be doubled for RCM with respect to that for RC1/RC2.

Photonic Reservoir Computing System for Pattern Recognition Based on an Array of Four Distributed Feedback Lasers

“…To overcome this drawback, it is necessary and challenging to develop an integrated time-delayed P-RC system to solve more complex tasks. In 2021, Yue et al implemented a parallel delayed RC system based on two SLs using electronic injection technology, with an NMSE value below 0.03 and a data processing rate of 28 GSa/s . In 2023, Hou et al proposed the parallel reservoirs of the multiple RC used for handwritten digit recognition tasks, with a recognition accuracy of 0.978 .…”

“…In 2021, Yue et al implemented a parallel delayed RC system based on two SLs using electronic injection technology, with an NMSE value below 0.03 and a data processing rate of 28 GSa/s. 36 In 2023, Hou et al proposed the parallel reservoirs of the multiple RC used for handwritten digit recognition tasks, with a recognition accuracy of 0.978. 37 Subsequently, Zhong et al have demonstrated a P-RC system with three laterally coupled lasers that could reproduce the nonlinear dynamics 38 In 2023, Zou et al proposed a nonfeedback method that utilized the pulse broadening effect induced by optical dispersion to implement a reservior layer by computing the multiplication of the modulator with the summation of the pulse temporal integration of the distributed feedback-laser diode.…”

Nanophotonic reservoir computing for COVID-19 pandemic forecasting

“…Compared with the above methods in this scenario, the advantages of the reservoir computing (RC) only training output weights via linear regression algorithm and randomly fixing connection weights to simplify the learning algorithm with lower calculation consumption and faster learning process are relatively prominent [ 19 – 21 ]. Furthermore, RC is applicable for hardware implementations with its robustness against fabrication tolerances, and RC has demonstrated advanced property on a series of complicated assignments with time-dependent data [ 22 ].…”

Nanophotonic Reservoir Computing for COVID-19 Pandemic Forecasting

“…Compared with the above methods in this scenario, the advantages of the reservoir computing (RC) only training output weights via linear regression algorithm and randomly fixing connection weights to simplify the learning algorithm are relatively prominent [19][20][21]. Furthermore, RC is applicable for hardware implementations with its robustness against fabrication tolerances, and RC has demonstrated advanced property on a series of complicated assignments with time-dependent data [22].…”