Evolutionary extreme learning machine based on an improved MOPSO algorithm
Extreme learning machine (ELM), as a single hidden layer feedforward neural network (SLFN), has attracted extensive attention because of its fast learning speed and high accuracy. However, the random selection of input weights and hidden biases is the main reason that deteriorates the generalization...
Uloženo v:
| Vydáno v: | Neural computing & applications Ročník 37; číslo 12; s. 7733 - 7750 |
|---|---|
| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
London
Springer London
01.04.2025
Springer Nature B.V |
| Témata: | |
| ISSN: | 0941-0643, 1433-3058 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Extreme learning machine (ELM), as a single hidden layer feedforward neural network (SLFN), has attracted extensive attention because of its fast learning speed and high accuracy. However, the random selection of input weights and hidden biases is the main reason that deteriorates the generalization performance and stability of ELM network. In this study, an improved ELM (IMOPSO-ELM) is proposed to enhance the generalization performance and convergence stability of the SLFN by using a multi-objective particle swarm optimization (MOPSO) to determine the input parameters including input weights and hidden biases of the SLFN. Firstly, different from the traditional improved ELM based on single-objective evolutionary algorithm, the proposed algorithm used MOPSO to optimize the input weights and hidden biases of SLFN by considering the two objectives including accuracy on the validation set and the 2-norm of the SLFN output weights. Secondly, in order to improve the diversity and convergence of the solution set obtained by MOPSO, an improved MOPSO (IMOPSO) is proposed. The improved MOPSO uses a new optimal global particle selection strategy, by randomly dividing the population into several subpopulations, each subpopulation uses different particle information in the external archive to guide the subpopulation update, and uses the external archive set as the platform to share the information between sub-swarms. Finally, the experiment on the four regression problems and four classification problems verifies the effectiveness of the approach in improving ELM generalization performance and performance stability. |
|---|---|
| AbstractList | Extreme learning machine (ELM), as a single hidden layer feedforward neural network (SLFN), has attracted extensive attention because of its fast learning speed and high accuracy. However, the random selection of input weights and hidden biases is the main reason that deteriorates the generalization performance and stability of ELM network. In this study, an improved ELM (IMOPSO-ELM) is proposed to enhance the generalization performance and convergence stability of the SLFN by using a multi-objective particle swarm optimization (MOPSO) to determine the input parameters including input weights and hidden biases of the SLFN. Firstly, different from the traditional improved ELM based on single-objective evolutionary algorithm, the proposed algorithm used MOPSO to optimize the input weights and hidden biases of SLFN by considering the two objectives including accuracy on the validation set and the 2-norm of the SLFN output weights. Secondly, in order to improve the diversity and convergence of the solution set obtained by MOPSO, an improved MOPSO (IMOPSO) is proposed. The improved MOPSO uses a new optimal global particle selection strategy, by randomly dividing the population into several subpopulations, each subpopulation uses different particle information in the external archive to guide the subpopulation update, and uses the external archive set as the platform to share the information between sub-swarms. Finally, the experiment on the four regression problems and four classification problems verifies the effectiveness of the approach in improving ELM generalization performance and performance stability. |
| Author | Tan, Kaimin Liu, Wenkai Ling, Qinghua Li, Zexu Wang, Yuyan |
| Author_xml | – sequence: 1 givenname: Qinghua surname: Ling fullname: Ling, Qinghua email: jsjxy_lqh@just.edu.cn organization: School of Computer Science, Jiangsu University of Science and Technology – sequence: 2 givenname: Kaimin surname: Tan fullname: Tan, Kaimin organization: School of Computer Science, Jiangsu University of Science and Technology – sequence: 3 givenname: Yuyan surname: Wang fullname: Wang, Yuyan organization: School of Computer Science, Jiangsu University of Science and Technology – sequence: 4 givenname: Zexu surname: Li fullname: Li, Zexu organization: School of Computer Science, Jiangsu University of Science and Technology – sequence: 5 givenname: Wenkai surname: Liu fullname: Liu, Wenkai organization: School of Computer Science, Jiangsu University of Science and Technology |
| BookMark | eNp9kF1LwzAUhoNMcJv-Aa8CXldPmtO0vZQxv5hMUK9DlqVbRpvOpBv6782sIHixqxDyPue8eUZk4FpnCLlkcM0A8psAkKUsgRQTBlleJHhChgw5TzhkxYAMocT4LJCfkVEIGwBAUWRD8jTdt_Wus61T_ouaz86bxtDaKO-sW9FG6bV1hi5UMEvaOqoctc3Wt_t4fZ6_vM6pqlett926OSenlaqDufg9x-T9bvo2eUhm8_vHye0s0TwWSJgoWWFyVmhgqKDIU6MrnZeodVoKqNK8QlgsM41MC0C2RJVxBJUClgs0mo_JVT831vjYmdDJTbvzLq6UnBVCCGQij6miT2nfhuBNJbXt1OGjnVe2lgzkwZzszcloTv6YkxjR9B-69baJfo5DvIdCDLuV8X-tjlDfBqKBSw |
| CitedBy_id | crossref_primary_10_3390_en18184952 crossref_primary_10_1007_s00521_025_11519_5 |
| Cites_doi | 10.1109/TEVC.2006.886448 10.1109/IJCNN.2004.1380068 10.1109/MHS.1995.494215 10.1109/TNNLS.2020.3027293 10.1016/j.ijhydene.2021.06.046 10.1016/j.neucom.2005.12.126 10.1162/106365600568167 10.1109/TNN.2006.875977 10.1007/s10489-018-1322-z 10.1109/TITS.2021.3086808 10.1109/TCYB.2019.2922287 10.1016/j.energy.2022.123773 10.1016/j.jclepro.2022.130414 10.1016/j.engappai.2010.06.009 10.1016/j.neucom.2011.12.062 10.1109/TEVC.2004.826067 10.1109/TCYB.2020.3015756 10.1162/106365600568202 10.1109/IJCNN.2013.6706751 10.1109/18.661502 10.1007/s10489-021-02665-z 10.1109/TCYB.2019.2949204 10.1007/s10489-018-1282-3 10.1016/j.eswa.2021.115579 10.1109/TEVC.2012.2227145 10.1109/TEVC.2018.2875430 10.1007/s10489-022-04284-8 10.1016/j.neucom.2014.10.006 10.1016/j.patcog.2005.03.028 10.1109/ICACI.2015.7184751 10.1016/j.est.2022.104996 |
| ContentType | Journal Article |
| Copyright | The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024 Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Copyright Springer Nature B.V. Apr 2025 |
| Copyright_xml | – notice: The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024 Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. – notice: Copyright Springer Nature B.V. Apr 2025 |
| DBID | AAYXX CITATION |
| DOI | 10.1007/s00521-024-10578-4 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1433-3058 |
| EndPage | 7750 |
| ExternalDocumentID | 10_1007_s00521_024_10578_4 |
| GrantInformation_xml | – fundername: National Natural Science Foundation of China grantid: 61976108 funderid: http://dx.doi.org/10.13039/501100001809 |
| GroupedDBID | -Y2 -~C .4S .86 .DC .VR 06D 0R~ 0VY 123 1N0 1SB 2.D 203 28- 29N 2J2 2JN 2JY 2KG 2LR 2P1 2VQ 2~H 30V 4.4 406 408 409 40D 40E 53G 5QI 5VS 67Z 6NX 8FE 8FG 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AAOBN AAPKM AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDBE ABDBF ABDZT ABECU ABFTD ABFTV ABHLI ABHQN ABJNI ABJOX ABKCH ABKTR ABLJU ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACHSB ACHXU ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACSNA ACUHS ACZOJ ADHHG ADHIR ADHKG ADIMF ADKFA ADKNI ADKPE ADMLS ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFDZB AFEXP AFGCZ AFKRA AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHPBZ AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARAPS ARCSS ARMRJ ASPBG AVWKF AXYYD AYFIA AYJHY AZFZN B-. B0M BA0 BBWZM BDATZ BENPR BGLVJ BGNMA BSONS CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 EAD EAP EBLON EBS ECS EDO EIOEI EJD EMI EMK EPL ESBYG EST ESX F5P FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNWQR GQ7 GQ8 GXS H13 HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I-F I09 IHE IJ- IKXTQ ITM IWAJR IXC IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ KDC KOV KOW LAS LLZTM M4Y MA- N2Q N9A NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM P19 P2P P62 P9O PF0 PHGZT PT4 PT5 QOK QOS R4E R89 R9I RHV RIG RNI RNS ROL RPX RSV RZK S16 S1Z S26 S27 S28 S3B SAP SCJ SCLPG SCO SDH SDM SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TSG TSK TSV TUC TUS U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WK8 YLTOR Z45 ZMTXR ~8M ~EX AAYXX ABBRH ABFSG ABRTQ ACSTC AEZWR AFFHD AFHIU AFOHR AGQPQ AHWEU AIXLP ATHPR CITATION PHGZM PQGLB |
| ID | FETCH-LOGICAL-c3064-16918e718c014a0872ecfc794cc2960f27f40bd5c41c6041d4a5340a2049b4ec3 |
| IEDL.DBID | RSV |
| ISSN | 0941-0643 |
| IngestDate | Wed Nov 05 08:27:40 EST 2025 Tue Nov 18 22:02:26 EST 2025 Sat Nov 29 08:03:13 EST 2025 Sun Apr 06 01:10:41 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 12 |
| Keywords | Generalization performance IMOPSO Extreme learning machine Performance stability |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c3064-16918e718c014a0872ecfc794cc2960f27f40bd5c41c6041d4a5340a2049b4ec3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| PQID | 3186664167 |
| PQPubID | 2043988 |
| PageCount | 18 |
| ParticipantIDs | proquest_journals_3186664167 crossref_citationtrail_10_1007_s00521_024_10578_4 crossref_primary_10_1007_s00521_024_10578_4 springer_journals_10_1007_s00521_024_10578_4 |
| PublicationCentury | 2000 |
| PublicationDate | 20250400 |
| PublicationDateYYYYMMDD | 2025-04-01 |
| PublicationDate_xml | – month: 4 year: 2025 text: 20250400 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: Heidelberg |
| PublicationTitle | Neural computing & applications |
| PublicationTitleAbbrev | Neural Comput & Applic |
| PublicationYear | 2025 |
| Publisher | Springer London Springer Nature B.V |
| Publisher_xml | – name: Springer London – name: Springer Nature B.V |
| References | D Li (10578_CR13) 2022; 250 M Zhang (10578_CR11) 2021; 46 F Han (10578_CR24) 2022; 52 XF Liu (10578_CR26) 2019; 23 Y Hu (10578_CR29) 2020; 51 WB Langdon (10578_CR16) 2007; 11 B Wu (10578_CR25) 2021; 51 C Fernández (10578_CR8) 2019; 49 QY Zhu (10578_CR3) 2005; 38 C Zhao (10578_CR30) 2021; 32 W Chen (10578_CR5) 2022; 53 10578_CR33 10578_CR15 H Lin (10578_CR28) 2023; 23 10578_CR34 Y Wang (10578_CR6) 2019; 49 ZA Xue (10578_CR7) 2020; 15 S Yang (10578_CR22) 2013; 17 10578_CR19 P Bartlett (10578_CR31) 1998; 44 Y Xu (10578_CR17) 2006 JD Knowles (10578_CR23) 2000; 8 S Suresh (10578_CR9) 2010; 23 GB Huang (10578_CR2) 2006; 17 W Sun (10578_CR14) 2022; 338 L Li (10578_CR27) 2019; 51 F Han (10578_CR18) 2013; 116 10578_CR1 E Zitzler (10578_CR32) 2000; 8 J Dou (10578_CR35) 2022; 52 10578_CR20 CAC Coello (10578_CR21) 2004; 8 GB Huang (10578_CR4) 2006; 70 D Wang (10578_CR10) 2015; 151 LL Li (10578_CR12) 2021; 184 |
| References_xml | – volume: 15 start-page: 41 year: 2020 ident: 10578_CR7 publication-title: Electr Eng – volume: 11 start-page: 561 issue: 5 year: 2007 ident: 10578_CR16 publication-title: IEEE Trans Evol Comput doi: 10.1109/TEVC.2006.886448 – ident: 10578_CR1 doi: 10.1109/IJCNN.2004.1380068 – ident: 10578_CR34 – ident: 10578_CR15 doi: 10.1109/MHS.1995.494215 – volume: 32 start-page: 5179 issue: 11 year: 2021 ident: 10578_CR30 publication-title: IEEE Trans Neural Netw Learn Syst doi: 10.1109/TNNLS.2020.3027293 – volume: 46 start-page: 28270 issue: 55 year: 2021 ident: 10578_CR11 publication-title: Int J Hydrog Energy doi: 10.1016/j.ijhydene.2021.06.046 – volume: 70 start-page: 489 issue: 1–3 year: 2006 ident: 10578_CR4 publication-title: Neurocomputing doi: 10.1016/j.neucom.2005.12.126 – volume: 8 start-page: 149 issue: 2 year: 2000 ident: 10578_CR23 publication-title: Evol Comput doi: 10.1162/106365600568167 – volume: 17 start-page: 879 issue: 4 year: 2006 ident: 10578_CR2 publication-title: IEEE Trans Neural Netw doi: 10.1109/TNN.2006.875977 – volume: 49 start-page: 1161 year: 2019 ident: 10578_CR6 publication-title: Appl Intell doi: 10.1007/s10489-018-1322-z – volume: 23 start-page: 16786 issue: 9 year: 2023 ident: 10578_CR28 publication-title: IEEE Trans Intell Transp Syst doi: 10.1109/TITS.2021.3086808 – volume: 51 start-page: 2055 issue: 4 year: 2019 ident: 10578_CR27 publication-title: IEEE Trans Cybernet doi: 10.1109/TCYB.2019.2922287 – volume: 250 start-page: 123773 year: 2022 ident: 10578_CR13 publication-title: Energy doi: 10.1016/j.energy.2022.123773 – volume: 338 start-page: 130414 year: 2022 ident: 10578_CR14 publication-title: J Clean Prod doi: 10.1016/j.jclepro.2022.130414 – volume: 23 start-page: 1149 issue: 7 year: 2010 ident: 10578_CR9 publication-title: Eng Appl Artif Intell doi: 10.1016/j.engappai.2010.06.009 – volume: 116 start-page: 87 year: 2013 ident: 10578_CR18 publication-title: Neurocomputing doi: 10.1016/j.neucom.2011.12.062 – volume: 8 start-page: 256 issue: 3 year: 2004 ident: 10578_CR21 publication-title: IEEE Trans Evol Comput doi: 10.1109/TEVC.2004.826067 – volume: 51 start-page: 874 issue: 2 year: 2020 ident: 10578_CR29 publication-title: IEEE Trans Cybernet doi: 10.1109/TCYB.2020.3015756 – volume: 8 start-page: 173 issue: 2 year: 2000 ident: 10578_CR32 publication-title: Evol Comput doi: 10.1162/106365600568202 – ident: 10578_CR33 – ident: 10578_CR20 doi: 10.1109/IJCNN.2013.6706751 – volume: 44 start-page: 525 issue: 2 year: 1998 ident: 10578_CR31 publication-title: IEEE Trans Inform Theory doi: 10.1109/18.661502 – volume: 52 start-page: 5784 year: 2022 ident: 10578_CR24 publication-title: Appl Intell doi: 10.1007/s10489-021-02665-z – volume: 51 start-page: 3738 issue: 7 year: 2021 ident: 10578_CR25 publication-title: IEEE Trans Cybernet doi: 10.1109/TCYB.2019.2949204 – volume: 49 start-page: 532 year: 2019 ident: 10578_CR8 publication-title: Appl Intell doi: 10.1007/s10489-018-1282-3 – volume: 184 start-page: 115579 year: 2021 ident: 10578_CR12 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2021.115579 – volume: 17 start-page: 721 issue: 5 year: 2013 ident: 10578_CR22 publication-title: IEEE Trans Evol Comput doi: 10.1109/TEVC.2012.2227145 – volume: 23 start-page: 587 issue: 4 year: 2019 ident: 10578_CR26 publication-title: IEEE Trans Evol Comput doi: 10.1109/TEVC.2018.2875430 – volume: 53 start-page: 15476 issue: 12 year: 2022 ident: 10578_CR5 publication-title: Appl Intell doi: 10.1007/s10489-022-04284-8 – volume: 151 start-page: 883 year: 2015 ident: 10578_CR10 publication-title: Neurocomputing doi: 10.1016/j.neucom.2014.10.006 – volume: 38 start-page: 1759 issue: 10 year: 2005 ident: 10578_CR3 publication-title: Pattern Recognit doi: 10.1016/j.patcog.2005.03.028 – start-page: 644 volume-title: International symposium on neural networks year: 2006 ident: 10578_CR17 – ident: 10578_CR19 doi: 10.1109/ICACI.2015.7184751 – volume: 52 start-page: 104996 year: 2022 ident: 10578_CR35 publication-title: J Energy Storage doi: 10.1016/j.est.2022.104996 |
| SSID | ssj0004685 |
| Score | 2.3765984 |
| Snippet | Extreme learning machine (ELM), as a single hidden layer feedforward neural network (SLFN), has attracted extensive attention because of its fast learning... |
| SourceID | proquest crossref springer |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 7733 |
| SubjectTerms | Algorithms Archives & records Artificial Intelligence Artificial neural networks Bias Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Convergence Data Mining and Knowledge Discovery Evolutionary algorithms Image Processing and Computer Vision Machine learning Multiple objective analysis Particle swarm optimization Probability and Statistics in Computer Science S.I.: From Theory to Practice: Real-World Applications of AI in Data Science Special Issue on From Theory to Practice: Real-World Applications of AI in Data Science Stability |
| Title | Evolutionary extreme learning machine based on an improved MOPSO algorithm |
| URI | https://link.springer.com/article/10.1007/s00521-024-10578-4 https://www.proquest.com/docview/3186664167 |
| Volume | 37 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAVX databaseName: Springer Journals New Starts & Take-Overs Collection customDbUrl: eissn: 1433-3058 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0004685 issn: 0941-0643 databaseCode: RSV dateStart: 19970101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3fS8MwED50-uCL8ydOp-TBNw20adLWR5ENEd2GU9lbSdNkDrZOujnwvzfJ0k1FBX0sTUO53OXuS-7uAzjNSBRxRSPMFDUAJctwqiTDAVNah1QoQ2FZS26jVivu9S46rihsUma7l1eSdqdeFLuZE0wNfQnFhps2xnQV1rS7iw1hw3336UM1pCXi1LjF5PTQwJXKfD_HZ3e0jDG_XItab9Os_u8_t2DTRZfocq4O27Ai8x2olswNyBnyLtw0Zk7lePGG9P5sTgmRY5Doo5HNsJTIuLgMjXPEczSwpw_68a7d6bYRH_bHxWD6PNqDx2bj4eoaO1YFLAzawKY7Tiy1SxIaHXEvjogUSmizFIJoOKNIpKiXZkxQX4Qe9TPKWUA9TjSWSKkUwT5U8nEuDwClTPgBZcrnyuAak6pKCVHU5M0GGSM18EvhJsK1HDfMF8Nk0SzZCivRwkqssBJag7PFNy_zhhu_jq6Xa5Y445skgWniF-pIM6rBeblGy9c_z3b4t-FHsEEMG7DN46lDZVq8ymNYF7PpYFKcWKV8B5YP1-8 |
| linkProvider | Springer Nature |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3dS8MwED90Cvri_MTp1Dz4poU2TT98FNmYui_clL2FNk3mYOukmwP_e5Ms3VRU0MfSNJTLXe5-yd39AM4THASRIIHlCaIASpJYseCe5XpC6pDwuc80a0k9aDbDXu-qbYrCJnm2e34lqXfqRbGbOsGU0BcTS3HThhZZhTUiPZbqmP_QefpQDamJOCVuUTk9xDWlMt_P8dkdLWPML9ei2ttUi__7z23YMtElup6rww6s8HQXijlzAzKGvAd3lZlRuSh7Q3J_VqeEyDBI9NFIZ1hypFxcgsYpilI00KcP8rHRandaKBr2x9lg-jzah8dqpXtTswyrgsUU2rBUd5yQS5fEJDqK7DDAnAkmzZIxLOGMwIEgdpx4jDjMt4mTkMhziR1hiSViwpl7AIV0nPJDQLHHHJd4womEwjUqVZVgLIjKm3UTD5fAyYVLmWk5rpgvhnTRLFkLi0phUS0sSkpwsfjmZd5w49fR5XzNqDG-CXVVEz9fRppBCS7zNVq-_nm2o78NP4ONWrdRp_Xb5v0xbGLFDKxzespQmGav_ATW2Ww6mGSnWkHfAb4o2tM |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1bT8IwFD5RNMYX8RpR1D74potb1118NArxgkCCGt6WrWuRBAaBSeK_t6dsgEZNjI_L2mY5Pd3p137nfACnMfW8UDLPcCRDgBLHRiSFY9iOVD4kXeFyrVpS8-p1v92-bC5k8Wu2e34lOc1pwCpNSXoxjOXFLPENTzMVDKbMQJ1a32DLsMKQSI94vfWykBmpRTkVhkF-D7OztJnvx_gcmub7zS9XpDryVIv__-ZN2Mh2neRq6iZbsCSSbSjmig4kW-A7cF-ZZK4Yjt6J-m_j6SHJlCU6pK-Zl4Jg6IvJICFhQrr6VEI9PjaarQYJe53BqJu-9nfhuVp5ur41MrUFgyMKMbBqji9UqOIKNYWm71HBJVfLlXOqYI6knmRmFDucWdw1mRWz0LGZGVKFMSImuL0HhWSQiH0gkcMtmznSCiXiHaSwMkolQz6tHTu0BFZu6IBnpchREaMXzIooa2MFyliBNlbASnA26zOcFuL4tXU5n78gW5TjwMbifq7agXolOM_na_7659EO_tb8BNaaN9Wgdld_OIR1ioLBmupThkI6ehNHsMonaXc8Ota--gH6RuO3 |
| 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=Evolutionary+extreme+learning+machine+based+on+an+improved+MOPSO+algorithm&rft.jtitle=Neural+computing+%26+applications&rft.au=Ling%2C+Qinghua&rft.au=Tan%2C+Kaimin&rft.au=Wang%2C+Yuyan&rft.au=Li%2C+Zexu&rft.date=2025-04-01&rft.issn=0941-0643&rft.eissn=1433-3058&rft.volume=37&rft.issue=12&rft.spage=7733&rft.epage=7750&rft_id=info:doi/10.1007%2Fs00521-024-10578-4&rft.externalDBID=n%2Fa&rft.externalDocID=10_1007_s00521_024_10578_4 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0941-0643&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0941-0643&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0941-0643&client=summon |