Parallel information processing by a reservoir computing system based on a VCSEL subject to double optical feedback and optical injection
In this work, we propose a scheme of reservoir computing (RC) for processing a Santa-Fe time series prediction task and a signal classification task in parallel, and the performances of the RC have been numerically investigated. For this scheme, a vertical-cavity surface-emitting laser (VCSEL) simul...
Saved in:
Published in | Optics express Vol. 27; no. 18; p. 26070 |
---|---|
Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
United States
02.09.2019
|
Online Access | Get full text |
Cover
Loading…
Abstract | In this work, we propose a scheme of reservoir computing (RC) for processing a Santa-Fe time series prediction task and a signal classification task in parallel, and the performances of the RC have been numerically investigated. For this scheme, a vertical-cavity surface-emitting laser (VCSEL) simultaneously subject to double optical feedback and optical injection is utilized as a nonlinear node, and the parallel information processing of the RC system is implemented based on the dynamical responses of X polarization component (X-PC) and Y polarization component (Y-PC) in the VCSEL. Considering that two different feedback frames (polarization-preserved optical feedback (PP-OF) or polarization-rotated optical feedback (PR-OF)) may be adopted in two feedback loops, four feedback combination cases are numerically analyzed. The simulated results show that the parallel processing ability of the proposed RC system depends on the feedback frames adopted in two loops. After comprehensively evaluating the parallel processing performances of the two tasks under different feedback combinations, the best parallel processing performance can be achieved by adopting PP-OFs in both two feedback loops. Under some optimized operation parameters, this proposed RC system can realize the lowest prediction error of 0.0289 and the lowest signal classification error of 2.78 × 10
. |
---|---|
AbstractList | In this work, we propose a scheme of reservoir computing (RC) for processing a Santa-Fe time series prediction task and a signal classification task in parallel, and the performances of the RC have been numerically investigated. For this scheme, a vertical-cavity surface-emitting laser (VCSEL) simultaneously subject to double optical feedback and optical injection is utilized as a nonlinear node, and the parallel information processing of the RC system is implemented based on the dynamical responses of X polarization component (X-PC) and Y polarization component (Y-PC) in the VCSEL. Considering that two different feedback frames (polarization-preserved optical feedback (PP-OF) or polarization-rotated optical feedback (PR-OF)) may be adopted in two feedback loops, four feedback combination cases are numerically analyzed. The simulated results show that the parallel processing ability of the proposed RC system depends on the feedback frames adopted in two loops. After comprehensively evaluating the parallel processing performances of the two tasks under different feedback combinations, the best parallel processing performance can be achieved by adopting PP-OFs in both two feedback loops. Under some optimized operation parameters, this proposed RC system can realize the lowest prediction error of 0.0289 and the lowest signal classification error of 2.78 × 10-5.In this work, we propose a scheme of reservoir computing (RC) for processing a Santa-Fe time series prediction task and a signal classification task in parallel, and the performances of the RC have been numerically investigated. For this scheme, a vertical-cavity surface-emitting laser (VCSEL) simultaneously subject to double optical feedback and optical injection is utilized as a nonlinear node, and the parallel information processing of the RC system is implemented based on the dynamical responses of X polarization component (X-PC) and Y polarization component (Y-PC) in the VCSEL. Considering that two different feedback frames (polarization-preserved optical feedback (PP-OF) or polarization-rotated optical feedback (PR-OF)) may be adopted in two feedback loops, four feedback combination cases are numerically analyzed. The simulated results show that the parallel processing ability of the proposed RC system depends on the feedback frames adopted in two loops. After comprehensively evaluating the parallel processing performances of the two tasks under different feedback combinations, the best parallel processing performance can be achieved by adopting PP-OFs in both two feedback loops. Under some optimized operation parameters, this proposed RC system can realize the lowest prediction error of 0.0289 and the lowest signal classification error of 2.78 × 10-5. In this work, we propose a scheme of reservoir computing (RC) for processing a Santa-Fe time series prediction task and a signal classification task in parallel, and the performances of the RC have been numerically investigated. For this scheme, a vertical-cavity surface-emitting laser (VCSEL) simultaneously subject to double optical feedback and optical injection is utilized as a nonlinear node, and the parallel information processing of the RC system is implemented based on the dynamical responses of X polarization component (X-PC) and Y polarization component (Y-PC) in the VCSEL. Considering that two different feedback frames (polarization-preserved optical feedback (PP-OF) or polarization-rotated optical feedback (PR-OF)) may be adopted in two feedback loops, four feedback combination cases are numerically analyzed. The simulated results show that the parallel processing ability of the proposed RC system depends on the feedback frames adopted in two loops. After comprehensively evaluating the parallel processing performances of the two tasks under different feedback combinations, the best parallel processing performance can be achieved by adopting PP-OFs in both two feedback loops. Under some optimized operation parameters, this proposed RC system can realize the lowest prediction error of 0.0289 and the lowest signal classification error of 2.78 × 10 . |
Author | Tan, XiangSheng Wu, ZhengMao Hou, YuShuang Xia, GuangQiong |
Author_xml | – sequence: 1 givenname: XiangSheng surname: Tan fullname: Tan, XiangSheng – sequence: 2 givenname: YuShuang surname: Hou fullname: Hou, YuShuang – sequence: 3 givenname: ZhengMao orcidid: 0000-0002-4331-8743 surname: Wu fullname: Wu, ZhengMao – sequence: 4 givenname: GuangQiong orcidid: 0000-0002-3920-6749 surname: Xia fullname: Xia, GuangQiong |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31510467$$D View this record in MEDLINE/PubMed |
BookMark | eNptkU1P4zAQhi3ECii7N87IRw60azuOnRxRVT6kSl1pF66RPybI4MTFdpD6E_jXpBQQQnua0cwzM5r3naD9PvSA0AklM1oI_nu1mDE5I0wQSfbQESU1n3JSyf0v-SGapPRACOWylgfosKAlJVzII_TyR0XlPXjs-jbETmUXeryOwUBKrr_HeoMVjpAgPgcXsQndesjbRtqkDB3WKoHF44zCd_O_iyVOg34Ak3EO2IZBe8BhnZ1RHrcAVivziFVvP4uu39Lj0Z_oR6t8gl_v8RjdXi7-za-ny9XVzfxiOTWMczIVStaiqmklRAEFY2DLVttKS90SzUEoYaSirK0Np4JaYUvTUmYtIbqypSTFMTrb7R2ffBog5aZzyYD3qocwpIaxqi5lwctiRE_f0UF3YJt1dJ2Km-ZDvhE43wEmhpQitJ8IJc3WnWa1aJhsdu6MOPuGG5ffFM9ROf__oVdC_5NR |
CitedBy_id | crossref_primary_10_1364_AO_477362 crossref_primary_10_1109_JPHOT_2025_3528019 crossref_primary_10_1364_PRJ_409114 crossref_primary_10_1364_AO_454422 crossref_primary_10_1364_OME_451585 crossref_primary_10_1364_OPTCON_453196 crossref_primary_10_1109_JLT_2024_3357745 crossref_primary_10_1364_OE_495697 crossref_primary_10_1016_j_optcom_2021_127120 crossref_primary_10_1109_JQE_2024_3416990 crossref_primary_10_1364_OE_491953 crossref_primary_10_1109_JLT_2024_3422327 crossref_primary_10_1364_OE_470857 crossref_primary_10_1364_OE_491910 crossref_primary_10_1364_OE_500065 crossref_primary_10_1109_ACCESS_2020_3017636 crossref_primary_10_3390_e22020231 crossref_primary_10_1016_j_optlastec_2022_108994 crossref_primary_10_1364_OE_527428 crossref_primary_10_1016_j_optlastec_2023_109200 crossref_primary_10_1109_JSTQE_2022_3216628 crossref_primary_10_1364_OE_464804 crossref_primary_10_1155_2022_2859567 crossref_primary_10_1364_AO_475139 crossref_primary_10_1364_OE_387277 crossref_primary_10_1364_AO_398580 crossref_primary_10_1109_JPHOT_2021_3115598 crossref_primary_10_7498_aps_70_20210355 crossref_primary_10_1007_s00340_024_08217_w crossref_primary_10_1109_JSTQE_2024_3480455 crossref_primary_10_1364_OE_471213 crossref_primary_10_1109_JLT_2024_3517145 crossref_primary_10_1063_1_5120788 crossref_primary_10_1109_JQE_2022_3173522 crossref_primary_10_2139_ssrn_4167500 crossref_primary_10_1038_s42005_024_01858_5 crossref_primary_10_1364_AO_430705 |
Cites_doi | 10.1007/s11071-018-4057-9 10.1080/08898480890513580 10.1063/1.5042342 10.1038/srep00287 10.1364/OPTICA.2.000438 10.1364/OE.26.005777 10.1109/JSTQE.2013.2241738 10.1364/OE.21.000012 10.1364/OE.20.022783 10.1109/3.572151 10.1364/OE.20.003241 10.1364/OL.43.004497 10.1364/OE.22.008672 10.1364/OE.24.001238 10.1364/OE.26.010211 10.1038/ncomms2368 10.1364/OL.42.000375 10.1126/science.1091277 10.1364/OE.17.020124 10.1364/OL.32.001629 10.1038/ncomms1476 10.1103/PhysRevLett.108.244101 10.1364/OE.24.008679 10.1109/TNNLS.2015.2404346 10.1177/107754603030750 10.1109/JLT.2011.2157460 10.1364/OE.22.010868 10.1109/JLT.2006.886064 10.1016/j.neunet.2007.04.003 10.1177/107754630100700807 10.1364/OL.44.000049 |
ContentType | Journal Article |
DBID | AAYXX CITATION NPM 7X8 |
DOI | 10.1364/OE.27.026070 |
DatabaseTitle | CrossRef PubMed MEDLINE - Academic |
DatabaseTitleList | MEDLINE - Academic PubMed |
Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Physics |
EISSN | 1094-4087 |
ExternalDocumentID | 31510467 10_1364_OE_27_026070 |
Genre | Journal Article |
GroupedDBID | --- 123 29N 2WC 8SL AAFWJ AAWJZ AAYXX ACGFO ADBBV AEDJG AENEX AFPKN AKGWG ALMA_UNASSIGNED_HOLDINGS ATHME AYPRP AZSQR AZYMN BAWUL BCNDV CITATION CS3 DIK DSZJF DU5 E3Z EBS EJD F5P GROUPED_DOAJ GX1 KQ8 M~E OFLFD OK1 OPJBK OPLUZ OVT P2P RNS ROL ROS TR2 TR6 XSB NPM 7X8 |
ID | FETCH-LOGICAL-c2440-6a7968918663e322ed5fbd8b7bf0b4e6a6c7a12f9c4161d6d5cf12dd00b8d5703 |
ISSN | 1094-4087 |
IngestDate | Fri Jul 11 04:52:14 EDT 2025 Thu Apr 03 07:04:16 EDT 2025 Tue Jul 01 04:04:43 EDT 2025 Thu Apr 24 22:53:52 EDT 2025 |
IsDoiOpenAccess | false |
IsOpenAccess | true |
IsPeerReviewed | true |
IsScholarly | true |
Issue | 18 |
Language | English |
LinkModel | OpenURL |
MergedId | FETCHMERGED-LOGICAL-c2440-6a7968918663e322ed5fbd8b7bf0b4e6a6c7a12f9c4161d6d5cf12dd00b8d5703 |
Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ORCID | 0000-0002-3920-6749 0000-0002-4331-8743 |
OpenAccessLink | https://doi.org/10.1364/oe.27.026070 |
PMID | 31510467 |
PQID | 2289573453 |
PQPubID | 23479 |
ParticipantIDs | proquest_miscellaneous_2289573453 pubmed_primary_31510467 crossref_primary_10_1364_OE_27_026070 crossref_citationtrail_10_1364_OE_27_026070 |
ProviderPackageCode | CITATION AAYXX |
PublicationCentury | 2000 |
PublicationDate | 2019-09-02 2019-Sep-02 20190902 |
PublicationDateYYYYMMDD | 2019-09-02 |
PublicationDate_xml | – month: 09 year: 2019 text: 2019-09-02 day: 02 |
PublicationDecade | 2010 |
PublicationPlace | United States |
PublicationPlace_xml | – name: United States |
PublicationTitle | Optics express |
PublicationTitleAlternate | Opt Express |
PublicationYear | 2019 |
References | Nguimdo (oe-27-18-26070-R20) 2014; 22 Nguimdo (oe-27-18-26070-R27) 2019; 44 Verstraeten (oe-27-18-26070-R9) 2007; 20 Hicke (oe-27-18-26070-R19) 2013; 19 Koyama (oe-27-18-26070-R31) 2006; 24 Boucekkine (oe-27-18-26070-R2) 2004; 11 Vinckier (oe-27-18-26070-R17) 2015; 2 Nguimdo (oe-27-18-26070-R21) 2016; 24 Henry (oe-27-18-26070-R3) 2001; 7 Paquot (oe-27-18-26070-R12) 2012; 2 Duport (oe-27-18-26070-R15) 2012; 20 Xiang (oe-27-18-26070-R30) 2011; 29 Nakayama (oe-27-18-26070-R22) 2016; 24 Wu (oe-27-18-26070-R34) 2009; 17 Brunner (oe-27-18-26070-R7) 2018; 124 Hou (oe-27-18-26070-R26) 2018; 26 Dejonckheere (oe-27-18-26070-R16) 2014; 22 Martin-Regalado (oe-27-18-26070-R32) 1997; 33 Soriano (oe-27-18-26070-R14) 2013; 21 Jaeger (oe-27-18-26070-R8) 2004; 304 Masoud (oe-27-18-26070-R4) 2003; 9 Nguimdo (oe-27-18-26070-R25) 2017; 42 Appeltant (oe-27-18-26070-R10) 2011; 2 Nguimdo (oe-27-18-26070-R24) 2015; 26 Brunner (oe-27-18-26070-R18) 2013; 4 Vatin (oe-27-18-26070-R29) 2018; 43 Li (oe-27-18-26070-R5) 2018; 92 Kuriki (oe-27-18-26070-R23) 2018; 26 Larger (oe-27-18-26070-R11) 2012; 20 Gatare (oe-27-18-26070-R33) 2007; 32 Martinenghi (oe-27-18-26070-R13) 2012; 108 |
References_xml | – volume: 92 start-page: 315 year: 2018 ident: oe-27-18-26070-R5 publication-title: Nonlinear Dyn. doi: 10.1007/s11071-018-4057-9 – volume: 11 start-page: 151 year: 2004 ident: oe-27-18-26070-R2 publication-title: Math. Popul. Stud. doi: 10.1080/08898480890513580 – volume: 124 start-page: 152004 year: 2018 ident: oe-27-18-26070-R7 publication-title: J. Appl. Phys. doi: 10.1063/1.5042342 – volume: 2 start-page: 287 year: 2012 ident: oe-27-18-26070-R12 publication-title: Sci. Rep. doi: 10.1038/srep00287 – volume: 2 start-page: 438 year: 2015 ident: oe-27-18-26070-R17 publication-title: Optica doi: 10.1364/OPTICA.2.000438 – volume: 26 start-page: 5777 year: 2018 ident: oe-27-18-26070-R23 publication-title: Opt. Express doi: 10.1364/OE.26.005777 – volume: 19 start-page: 1501610 year: 2013 ident: oe-27-18-26070-R19 publication-title: IEEE J. Sel. Top. Quantum Electron. doi: 10.1109/JSTQE.2013.2241738 – volume: 21 start-page: 12 year: 2013 ident: oe-27-18-26070-R14 publication-title: Opt. Express doi: 10.1364/OE.21.000012 – volume: 20 start-page: 22783 year: 2012 ident: oe-27-18-26070-R15 publication-title: Opt. Express doi: 10.1364/OE.20.022783 – volume: 33 start-page: 765 year: 1997 ident: oe-27-18-26070-R32 publication-title: IEEE J. Quantum Electron. doi: 10.1109/3.572151 – volume: 20 start-page: 3241 year: 2012 ident: oe-27-18-26070-R11 publication-title: Opt. Express doi: 10.1364/OE.20.003241 – volume: 43 start-page: 4497 year: 2018 ident: oe-27-18-26070-R29 publication-title: Opt. Lett. doi: 10.1364/OL.43.004497 – volume: 22 start-page: 8672 year: 2014 ident: oe-27-18-26070-R20 publication-title: Opt. Express doi: 10.1364/OE.22.008672 – volume: 24 start-page: 1238 year: 2016 ident: oe-27-18-26070-R21 publication-title: Opt. Express doi: 10.1364/OE.24.001238 – volume: 26 start-page: 10211 year: 2018 ident: oe-27-18-26070-R26 publication-title: Opt. Express doi: 10.1364/OE.26.010211 – volume: 4 start-page: 1364 year: 2013 ident: oe-27-18-26070-R18 publication-title: Nat. Commun. doi: 10.1038/ncomms2368 – volume: 42 start-page: 375 year: 2017 ident: oe-27-18-26070-R25 publication-title: Opt. Lett. doi: 10.1364/OL.42.000375 – volume: 304 start-page: 78 year: 2004 ident: oe-27-18-26070-R8 publication-title: Science doi: 10.1126/science.1091277 – volume: 17 start-page: 20124 year: 2009 ident: oe-27-18-26070-R34 publication-title: Opt. Express doi: 10.1364/OE.17.020124 – volume: 32 start-page: 1629 year: 2007 ident: oe-27-18-26070-R33 publication-title: Opt. Lett. doi: 10.1364/OL.32.001629 – volume: 2 start-page: 468 year: 2011 ident: oe-27-18-26070-R10 publication-title: Nat. Commun. doi: 10.1038/ncomms1476 – volume: 108 start-page: 244101 year: 2012 ident: oe-27-18-26070-R13 publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.108.244101 – volume: 24 start-page: 8679 year: 2016 ident: oe-27-18-26070-R22 publication-title: Opt. Express doi: 10.1364/OE.24.008679 – volume: 26 start-page: 3301 year: 2015 ident: oe-27-18-26070-R24 publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2015.2404346 – volume: 9 start-page: 257 year: 2003 ident: oe-27-18-26070-R4 publication-title: J. Vib. Control doi: 10.1177/107754603030750 – volume: 29 start-page: 2173 year: 2011 ident: oe-27-18-26070-R30 publication-title: J. Lightwave Technol. doi: 10.1109/JLT.2011.2157460 – volume: 22 start-page: 10868 year: 2014 ident: oe-27-18-26070-R16 publication-title: Opt. Express doi: 10.1364/OE.22.010868 – volume: 24 start-page: 4502 year: 2006 ident: oe-27-18-26070-R31 publication-title: J. Lightwave Technol. doi: 10.1109/JLT.2006.886064 – volume: 20 start-page: 391 year: 2007 ident: oe-27-18-26070-R9 publication-title: Neural Netw. doi: 10.1016/j.neunet.2007.04.003 – volume: 7 start-page: 1253 year: 2001 ident: oe-27-18-26070-R3 publication-title: J. Vib. Control doi: 10.1177/107754630100700807 – volume: 44 start-page: 49 year: 2019 ident: oe-27-18-26070-R27 publication-title: Opt. Lett. doi: 10.1364/OL.44.000049 |
SSID | ssj0014797 |
Score | 2.4701252 |
Snippet | In this work, we propose a scheme of reservoir computing (RC) for processing a Santa-Fe time series prediction task and a signal classification task in... |
SourceID | proquest pubmed crossref |
SourceType | Aggregation Database Index Database Enrichment Source |
StartPage | 26070 |
Title | Parallel information processing by a reservoir computing system based on a VCSEL subject to double optical feedback and optical injection |
URI | https://www.ncbi.nlm.nih.gov/pubmed/31510467 https://www.proquest.com/docview/2289573453 |
Volume | 27 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3Nb9MwFLfKEBIXxPfKx2QkOEUpSRw7yRFNhQlRCuoGFZfIsZ1tgJKqbRBw4M7_wB_Ls524GXTS4BJVT3Zc9f3q9_0eQo8zxWhBKfVTQZkfZyTzU0WFT4gkZSaJSE0Ef_KaHRzFL-d0Phj86mUtNetiJL5vrSv5H64CDfiqq2T_gbPupUCAz8BfeAKH4XkhHr_hSz0KRbfNcEWI3sKm_msXAKiW3NP1Rcsv9enSpI83Js3Z9m_2tAiTOlzAvXf7s_Erb9UU2i-jFVJZN7qoql60RY4g5QouPplgQ0c8rT6aTK6qr-JOF6bzs_q6cNkdxjNgrrc5oPF4dqJaeWnydhsjBprZScM35PeG-kGvnPC6o85tau8LvfItnHvcd1qENiur58cMwaoE07WVtWoLrb2cbeOADoRp_6plgR058pcQICwGzk3HoygZBW7Z2V7bf8hAl5loAnwszqfjPEpyu_sSuhyBEaLnY0x-jF2MKk7s6J7ua7dlFbD7af_sswrPOVaM0WYOr6NrrRmCn1lM3UADVd1EV0w6sFjdQj87ZOEesvAGWbj4hjl2yMIOWdgiCxtkYdjDsUEWbpGF1zW2yMItiHCHLAzIckSHrNvo6Pn4cP_Ab4d2-CLSeQKMJxlLM91HkSiQFkrSspBpkRRlUMSKcSYSHkZlJrRpLZmkogwjKYOgSKVuB3cH7VR1pXYRTnnAAqEru0vYGYecgjFPSAJKuoxUKYfI637ZXLQd7fVglc_5Ni4O0RO3emE7uZyz7lHHpByuWh0_45Wqm1UeRWlGExJTMkR3LffcmwhozgEoHfcueMp9dHXzv3iAdtbLRj0E9XZd7Bm30J7B2m9SY6fh |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Parallel+information+processing+by+a+reservoir+computing+system+based+on+a+VCSEL+subject+to+double+optical+feedback+and+optical+injection&rft.jtitle=Optics+express&rft.au=Tan%2C+XiangSheng&rft.au=Hou%2C+YuShuang&rft.au=Wu%2C+ZhengMao&rft.au=Xia%2C+GuangQiong&rft.date=2019-09-02&rft.issn=1094-4087&rft.eissn=1094-4087&rft.volume=27&rft.issue=18&rft.spage=26070&rft_id=info:doi/10.1364%2FOE.27.026070&rft.externalDBID=n%2Fa&rft.externalDocID=10_1364_OE_27_026070 |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1094-4087&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1094-4087&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1094-4087&client=summon |