Zhang, Ruibo, Luan, Tianxiang, Li, Shuo, Wang, Chao, Zhang, Ailing (2024) Enhancing Signal Recognition Accuracy in Delay-Based Optical Reservoir Computing: A Comparative Analysis of Training Algorithms. Electronics, 13 (11). Article Number 2202. ISSN 2079-9292. (doi:10.3390/electronics13112202) (KAR id:106530)
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Official URL: https://doi.org/10.3390/electronics13112202 |
Abstract
To improve the accuracy of signal recognition in delay-based optical reservoir computing (RC) systems, this paper proposes the use of nonlinear algorithms at the output layer to replace traditional linear algorithms for training and testing datasets and apply them to the identification of frequency-modulated continuous wave (FMCW) LiDAR signals. This marks the inaugural use of the system for the identification of FMCW LiDAR signals. We elaborate on the fundamental principles of a delay-based optical RC system using an optical-injected distributed feedback laser (DFB) laser and discriminate four FMCW LiDAR signals through this setup. In the output layer, three distinct training algorithms—namely linear regression, support vector machine (SVM), and random forest—were employed to train the optical reservoir. Upon analyzing the experimental results, it was found that regardless of the size of the dataset, the recognition accuracy of the two nonlinear training algorithms was superior to that of the linear regression algorithm. Among the two nonlinear algorithms, the Random Forest algorithm had a higher recognition accuracy than SVM when the sample size was relatively small.
Item Type: | Article |
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DOI/Identification number: | 10.3390/electronics13112202 |
Uncontrolled keywords: | machine learning, delay-based optical reservoir computing, semiconductor laser, signal recognition |
Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts |
Funders: | University of Kent (https://ror.org/00xkeyj56) |
SWORD Depositor: | JISC Publications Router |
Depositing User: | JISC Publications Router |
Date Deposited: | 10 Jul 2024 14:05 UTC |
Last Modified: | 05 Nov 2024 13:12 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/106530 (The current URI for this page, for reference purposes) |
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