Reservoir Computing Based on Semiconductor Lasers Using Parallel Double Optical Feedback Structure

Author(s):  
Shuai Wang ◽  
Fei Hua ◽  
Nian Fang ◽  
Lutang Wang
2021 ◽  
Author(s):  
Dong-Zhou Zhong ◽  
Zhe Xu ◽  
Ya-Lan Hu ◽  
Ke-Ke Zhao ◽  
Jin-Bo Zhang ◽  
...  

Abstract In this work, we utilize three parallel reservoir computers using semiconductor lasers with optical feedback and light injection to model radar probe signals with delays. Three radar probe signals are generated by driving lasers constructed by a three-element lase array with self-feedback. The response lasers are implemented also by a three-element lase array with both delay-time feedback and optical injection, which are utilized as nonlinear nodes to realize the reservoirs. We show that each delayed radar probe signal can well be predicted and to synchronize with its corresponding trained reservoir, even when there exist parameter mismatches between the response laser array and the driving laser array. Based on this, the three synchronous probe signals are utilized for ranging to three targets, respectively, using Hilbert transform. It is demonstrated that the relative errors for ranging can be very small and less than 0.6%. Our findings show that optical reservoir computing provides an effective way for applications of target ranging.


Photonics ◽  
2019 ◽  
Vol 6 (4) ◽  
pp. 124 ◽  
Author(s):  
Krishan Harkhoe ◽  
Guy Van der Sande

Reservoir computing has rekindled neuromorphic computing in photonics. One of the simplest technological implementations of reservoir computing consists of a semiconductor laser with delayed optical feedback. In this delay-based scheme, virtual nodes are distributed in time with a certain node distance and form a time-multiplexed network. The information processing performance of a semiconductor laser-based reservoir computing (RC) system is usually analysed by way of testing the laser-based reservoir computer on specific benchmark tasks. In this work, we will illustrate the optimal performance of the system on a chaotic time-series prediction benchmark. However, the goal is to analyse the reservoir’s performance in a task-independent way. This is done by calculating the computational capacity, a measure for the total number of independent calculations that the system can handle. We focus on the dependence of the computational capacity on the specifics of the masking procedure. We find that the computational capacity depends strongly on the virtual node distance with an optimal node spacing of 30 ps. In addition, we show that the computational capacity can be further increased by allowing for a well chosen mismatch between delay and input data sample time.


2017 ◽  
Vol 25 (3) ◽  
pp. 2401 ◽  
Author(s):  
Julián Bueno ◽  
Daniel Brunner ◽  
Miguel C. Soriano ◽  
Ingo Fischer

1981 ◽  
Vol 17 (19) ◽  
pp. 677 ◽  
Author(s):  
L. Goldberg ◽  
A. Dandridge ◽  
R.O. Miles ◽  
T.G. Giallorenzi ◽  
J.F. Weller

1988 ◽  
Vol 24 (9) ◽  
pp. 509 ◽  
Author(s):  
J.L. Beylat ◽  
J. Jacquet

1983 ◽  
Vol 19 (22) ◽  
pp. 938 ◽  
Author(s):  
E. Patzak ◽  
H. Olesen ◽  
A. Sugimura ◽  
S. Saito ◽  
T. Mukai

1982 ◽  
Vol 18 (4) ◽  
pp. 555-564 ◽  
Author(s):  
L. Goldberg ◽  
H. Taylor ◽  
A. Dandridge ◽  
J. Weller ◽  
R. Miles

1988 ◽  
Vol 63 (7) ◽  
pp. 2200-2205 ◽  
Author(s):  
Hisanao Sato ◽  
Yasushi Matsui ◽  
Jun Ohya ◽  
Hiroyuki Serizawa

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