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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| Published in | Electronics (Basel) Vol. 11; no. 16; p. 2577 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
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MDPI AG
01.08.2022
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| ISSN | 2079-9292 2079-9292 |
| DOI | 10.3390/electronics11162577 |
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| Abstract | 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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| AbstractList | 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. |
| Audience | Academic |
| Author | Yang, Yigong Zhou, Pei Cai, Deyu Li, Nianqiang |
| Author_xml | – sequence: 1 givenname: Deyu surname: Cai fullname: Cai, Deyu – sequence: 2 givenname: Yigong surname: Yang fullname: Yang, Yigong – sequence: 3 givenname: Pei orcidid: 0000-0001-7570-9098 surname: Zhou fullname: Zhou, Pei – sequence: 4 givenname: Nianqiang surname: Li fullname: Li, Nianqiang |
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| Snippet | As an efficient information processing method, reservoir computing (RC) is essential to artificial neural networks (ANNs). Via the Santa Fe time series... |
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| SubjectTerms | 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 |
| Title | Enhanced Prediction Performance of Reservoir Computing Based on Mutually Delay-Coupled Semiconductor Lasers via Parameter Mismatch |
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