Enhanced Prediction Performance of Reservoir Computing Based on Mutually Delay-Coupled Semiconductor Lasers via Parameter Mismatch

As an efficient information processing method, reservoir computing (RC) is essential to artificial neural networks (ANNs). Via the Santa Fe time series prediction task, we numerically investigated the effect of the mismatch of some critical parameters on the prediction performance of the RC based on...

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Bibliographic Details
Published inElectronics (Basel) Vol. 11; no. 16; p. 2577
Main Authors Cai, Deyu, Yang, Yigong, Zhou, Pei, Li, Nianqiang
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.08.2022
Subjects
Algorithms
Artificial neural networks
Computation
Data processing
Electric fields
Information processing
Laser applications
Lasers
Machine learning
Neural networks
Parameters
Performance prediction
Prediction theory
Reservoir performance
Semiconductor lasers
Simulation
Time series
China
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Summary:As an efficient information processing method, reservoir computing (RC) is essential to artificial neural networks (ANNs). Via the Santa Fe time series prediction task, we numerically investigated the effect of the mismatch of some critical parameters on the prediction performance of the RC based on two mutually delay-coupled semiconductor lasers (SLs) with optical injection. The results show that better prediction performance can be realized by setting appropriate parameter mismatch scenarios. Especially for the situation with large prediction errors encountered in the RC with identical laser parameters, a suitable parameter mismatch setting can achieve computing performance improvement of an order of magnitude. Our research is instructive for the hardware implementation of laser-based RC, where the parameter mismatch is unavoidable.
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ISSN:2079-9292
2079-9292
DOI:10.3390/electronics11162577