Online sequential extreme learning machine based adaptive control for wastewater treatment plant
Wastewater Treatment Plant (WWTP) is challenging to regulate for its complex chemical and biological characteristics, and its precise mathematical model is usually not accessible due to the limitation of available measurement. Traditional methods highly rely on human intervention and cannot adapt to...
Uloženo v:
| Vydáno v: | Neurocomputing (Amsterdam) Ročník 408; s. 169 - 175 |
|---|---|
| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier B.V
30.09.2020
|
| Témata: | |
| ISSN: | 0925-2312, 1872-8286 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Wastewater Treatment Plant (WWTP) is challenging to regulate for its complex chemical and biological characteristics, and its precise mathematical model is usually not accessible due to the limitation of available measurement. Traditional methods highly rely on human intervention and cannot adapt to the varying environment. Meanwhile, adaptive neural network based control strategies are encountered with dilemmas of local minima, slow convergence, huge time consumption, and low efficiency. To overcome such challenges, in this paper, a novel Online Sequential Extreme Learning Machine (OS-ELM) based adaptive control is discussed. In contrast, no a prior human experience or off-line training phase is required. It also has fast training speed thanks to randomly generated parameters of the hidden layer and Moore–Penrose pseudo-inverse. Moreover, the performance of the system is guaranteed even in the presence of time-varying dynamics and uncertainties through a learning mechanism in an online manner. The stability of the closed-loop system is shown strictly and extensive comparison case studies substantiate the feasibility of the scheme. |
|---|---|
| AbstractList | Wastewater Treatment Plant (WWTP) is challenging to regulate for its complex chemical and biological characteristics, and its precise mathematical model is usually not accessible due to the limitation of available measurement. Traditional methods highly rely on human intervention and cannot adapt to the varying environment. Meanwhile, adaptive neural network based control strategies are encountered with dilemmas of local minima, slow convergence, huge time consumption, and low efficiency. To overcome such challenges, in this paper, a novel Online Sequential Extreme Learning Machine (OS-ELM) based adaptive control is discussed. In contrast, no a prior human experience or off-line training phase is required. It also has fast training speed thanks to randomly generated parameters of the hidden layer and Moore–Penrose pseudo-inverse. Moreover, the performance of the system is guaranteed even in the presence of time-varying dynamics and uncertainties through a learning mechanism in an online manner. The stability of the closed-loop system is shown strictly and extensive comparison case studies substantiate the feasibility of the scheme. |
| Author | Yang, Qinmin Cao, Weiwei |
| Author_xml | – sequence: 1 givenname: Weiwei orcidid: 0000-0002-2295-6340 surname: Cao fullname: Cao, Weiwei email: cww@zju.edu.cn – sequence: 2 givenname: Qinmin orcidid: 0000-0002-1602-8986 surname: Yang fullname: Yang, Qinmin email: qmyang@zju.edu.cn |
| BookMark | eNqFkM1OwzAQhC1UJNrCG3DwCySsncZJOCChij-pUi9wNo6zAVeJU2y3hbfHUTlxgNOuRvONNDMjEztYJOSSQcqAiatNanGnhz7lwKoU8qhWJ2TKyoInJS_FhEyh4nnCM8bPyMz7DQArGK-m5HVtO2ORevzYoQ1GdRQ_g8MeaYfKWWPfaK_0--iplceGqkZtg9kj1YMNbuhoOzh6UD7gQQV0NMIq9DGLbjtlwzk5bVXn8eLnzsnL_d3z8jFZrR-elrerRGcgQoI6Y4KDaJuiaTAXkNc8voXOo6JQAMtqvSihhloUdVlCocWiaDirlYY60nOyOOZqN3jvsJVbZ3rlviQDOa4kN_K4khxXkpBHtYrY9S9Mm6CCGbsp0_0H3xxhjMX2Bp302qDV2BiHOshmMH8HfANvGItZ |
| CitedBy_id | crossref_primary_10_1016_j_psep_2020_10_013 crossref_primary_10_3390_app132413023 crossref_primary_10_3390_app13084752 crossref_primary_10_1016_j_chemosphere_2024_142477 crossref_primary_10_1016_j_ins_2023_03_047 crossref_primary_10_1080_1573062X_2025_2559273 crossref_primary_10_2166_wst_2022_281 crossref_primary_10_1016_j_scitotenv_2021_147138 crossref_primary_10_1007_s11431_022_2403_8 crossref_primary_10_1016_j_jclepro_2025_145854 crossref_primary_10_1016_j_neucom_2021_06_074 crossref_primary_10_1016_j_jprocont_2022_06_004 crossref_primary_10_1016_j_bej_2023_109009 crossref_primary_10_1016_j_jenvman_2023_119230 crossref_primary_10_1080_00207721_2024_2414904 crossref_primary_10_1021_acsestengg_5c00493 crossref_primary_10_1109_TII_2023_3240937 crossref_primary_10_1016_j_rser_2023_113638 crossref_primary_10_1109_TETCI_2022_3230400 crossref_primary_10_1109_TCYB_2025_3552926 crossref_primary_10_1177_01423312211039048 crossref_primary_10_1016_j_cja_2023_08_012 crossref_primary_10_1111_wej_12945 crossref_primary_10_1016_j_jwpe_2024_106600 crossref_primary_10_1109_TFUZZ_2024_3369422 crossref_primary_10_1016_j_psep_2022_01_065 crossref_primary_10_1080_10643389_2023_2183699 crossref_primary_10_3390_w16020305 crossref_primary_10_1016_j_jclepro_2020_123811 crossref_primary_10_1016_j_jenvman_2024_120510 crossref_primary_10_1016_j_watres_2024_122179 crossref_primary_10_1016_j_aei_2022_101736 crossref_primary_10_1109_TII_2023_3296878 crossref_primary_10_1109_TSMC_2021_3122802 crossref_primary_10_1016_j_cej_2020_128070 crossref_primary_10_1016_j_cjche_2022_01_028 crossref_primary_10_3390_su151813802 crossref_primary_10_3390_toxics13050349 crossref_primary_10_1007_s13042_024_02422_x |
| Cites_doi | 10.1016/j.bej.2009.04.016 10.1016/j.neucom.2014.01.025 10.1109/TNNLS.2015.2470175 10.1016/j.compchemeng.2007.06.008 10.1109/TSMCB.2011.2166384 10.2175/106143098X124948 10.1016/j.neucom.2017.08.059 10.1109/TNN.2006.880583 10.4236/jwarp.2011.31001 10.1016/j.compchemeng.2011.09.011 10.2166/wst.1998.0210 10.2166/wst.2001.0422 10.1016/j.isatra.2016.07.012 10.1002/asjc.758 10.1016/j.asoc.2011.02.014 10.1016/j.conengprac.2012.01.001 10.1016/j.neucom.2005.12.126 10.1007/s00521-014-1738-2 10.1109/TIE.2013.2266086 10.1109/TNNLS.2015.2465174 10.1016/j.cej.2009.02.019 10.1016/j.cej.2005.05.004 10.1109/TNNLS.2013.2261574 10.1109/TAC.2019.2892391 10.1109/TNNLS.2016.2561300 |
| ContentType | Journal Article |
| Copyright | 2020 Elsevier B.V. |
| Copyright_xml | – notice: 2020 Elsevier B.V. |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.neucom.2019.05.109 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1872-8286 |
| EndPage | 175 |
| ExternalDocumentID | 10_1016_j_neucom_2019_05_109 S0925231220303416 |
| GrantInformation_xml | – fundername: National Natural Science Foundation of China grantid: U1609214; 61673347; 61751205 funderid: https://doi.org/10.13039/501100011002 – fundername: Key R&D Program of Guangdong Province grantid: 2018B010107002 funderid: https://doi.org/10.13039/501100012165 |
| GroupedDBID | --- --K --M .DC .~1 0R~ 123 1B1 1~. 1~5 4.4 457 4G. 53G 5VS 7-5 71M 8P~ 9JM 9JN AABNK AACTN AADPK AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAXLA AAXUO AAYFN ABBOA ABCQJ ABFNM ABJNI ABMAC ABYKQ ACDAQ ACGFS ACRLP ACZNC ADBBV ADEZE AEBSH AEKER AENEX AFKWA AFTJW AFXIZ AGHFR AGUBO AGWIK AGYEJ AHHHB AHZHX AIALX AIEXJ AIKHN AITUG AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD AXJTR BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EO8 EO9 EP2 EP3 F5P FDB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ IHE J1W KOM LG9 M41 MO0 MOBAO N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 ROL RPZ SDF SDG SDP SES SPC SPCBC SSN SSV SSZ T5K ZMT ~G- 29N 9DU AAQXK AATTM AAXKI AAYWO AAYXX ABWVN ABXDB ACLOT ACNNM ACRPL ACVFH ADCNI ADJOM ADMUD ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP ASPBG AVWKF AZFZN CITATION EFKBS EJD FEDTE FGOYB HLZ HVGLF HZ~ R2- SBC SEW WUQ XPP ~HD |
| ID | FETCH-LOGICAL-c306t-ec316206fd7dde5605b2d7d7c5fd7ae6013bc480b0b67b8807c647d21bac0bec3 |
| ISICitedReferencesCount | 43 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000571573400018&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0925-2312 |
| IngestDate | Sat Nov 29 07:12:59 EST 2025 Tue Nov 18 22:33:07 EST 2025 Fri Feb 23 02:49:22 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Wastewater treatment plant OS-ELM algorithm Adaptive control Benchmark simulation model no.1 (BSM1) |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c306t-ec316206fd7dde5605b2d7d7c5fd7ae6013bc480b0b67b8807c647d21bac0bec3 |
| ORCID | 0000-0002-2295-6340 0000-0002-1602-8986 |
| PageCount | 7 |
| ParticipantIDs | crossref_primary_10_1016_j_neucom_2019_05_109 crossref_citationtrail_10_1016_j_neucom_2019_05_109 elsevier_sciencedirect_doi_10_1016_j_neucom_2019_05_109 |
| PublicationCentury | 2000 |
| PublicationDate | 2020-09-30 |
| PublicationDateYYYYMMDD | 2020-09-30 |
| PublicationDate_xml | – month: 09 year: 2020 text: 2020-09-30 day: 30 |
| PublicationDecade | 2020 |
| PublicationTitle | Neurocomputing (Amsterdam) |
| PublicationYear | 2020 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Belchior, Araujo, Landeck (bib0032) 2012; 37 Yang, Wei, You, Chadli, Zhang (bib0007) 2014; 136 Han, Qiao (bib0014) 2013; 61 Suescun, Joaquin, Irizar, Ostolaza, Ayesa (bib0001) 1998; 70 Copp (bib0020) 2001 Francesco (bib0028) 2007 Punal, Rodriguez, Franco (bib0005) 2001; 43 Qiao, Han, Han (bib0009) 2014; 16 Traor, Grieu, Puig, Corominas, Thiery, Polit, Colprim (bib0004) 2005; 111 Wahab, Katebi, Balderud (bib0002) 2009; 45 Yang, Jagannathan, Sun (bib0017) 2015; 26 Qiao, Hou, Zhang, Han (bib0011) 2018; 275 Han, Qiao (bib0010) 2011; 11 Chong, Żak (bib0024) 2013 Han, Qiao (bib0012) 2012; 20 Ferrer, Rodrigo, Seco, Penya-Roja (bib0003) 1998; 38 Huang, Zhu, Siew (bib0023) 2006; 70 Fu, Qiao, Han, Meng (bib0033) 2015 Zhu, Peng, Ma, Yu, Yin (bib0006) 2009; 151 Fan, Yang, Jagannathan (bib0037) 2019; 64 Jia, Li, Wang, Ding (bib0026) 2016; 65 Zhu, Zhao, Li (bib0008) 2017; 28 Huang, Zhu, Siew (bib0022) 2004; 2 Alex, Benedetti, Copp, Garnaey, Jeppsson, Nopens, Pons, Rieger, Rosen, Steyer, Vanrolleghem, Winkler (bib0021) 2018 Li, Jia, Liu, Ding (bib0025) 2014; 2014 Liang, Huang, Saratchandran, Sundararajan (bib0019) 2006; 17 Le, Nguyen, Nguyen (bib0029) 2017 Yang, Jagannathan (bib36) 2012; 42 Zhu, Zhao (bib0016) 2015; 26 Han, Zhang, Hou, Qiao (bib0015) 2016; 27 Goodwin, Sin (bib0034) 1984 Vilanova, Katebi, Wahab (bib0030) 2011; 3 Holenda, Domokos, Redey, et al (bib0031) 2008; 32 Zhou (bib0027) 2017; 68 Han, Wu, Qiao (bib0013) 2013; 24 Han, Wang, Qiao (bib0018) 2014; 128 Huang (10.1016/j.neucom.2019.05.109_bib0023) 2006; 70 Copp (10.1016/j.neucom.2019.05.109_bib0020) 2001 Jia (10.1016/j.neucom.2019.05.109_bib0026) 2016; 65 Han (10.1016/j.neucom.2019.05.109_bib0014) 2013; 61 Huang (10.1016/j.neucom.2019.05.109_bib0022) 2004; 2 Chong (10.1016/j.neucom.2019.05.109_bib0024) 2013 Fu (10.1016/j.neucom.2019.05.109_bib0033) 2015 Li (10.1016/j.neucom.2019.05.109_bib0025) 2014; 2014 Holenda (10.1016/j.neucom.2019.05.109_bib0031) 2008; 32 Wahab (10.1016/j.neucom.2019.05.109_bib0002) 2009; 45 Yang (10.1016/j.neucom.2019.05.109_bib36) 2012; 42 Qiao (10.1016/j.neucom.2019.05.109_bib0009) 2014; 16 Han (10.1016/j.neucom.2019.05.109_bib0015) 2016; 27 Zhu (10.1016/j.neucom.2019.05.109_bib0016) 2015; 26 Liang (10.1016/j.neucom.2019.05.109_bib0019) 2006; 17 Ferrer (10.1016/j.neucom.2019.05.109_bib0003) 1998; 38 Fan (10.1016/j.neucom.2019.05.109_bib0037) 2019; 64 Yang (10.1016/j.neucom.2019.05.109_bib0007) 2014; 136 Zhou (10.1016/j.neucom.2019.05.109_bib0027) 2017; 68 Francesco (10.1016/j.neucom.2019.05.109_bib0028) 2007 Goodwin (10.1016/j.neucom.2019.05.109_bib0034) 1984 Han (10.1016/j.neucom.2019.05.109_bib0013) 2013; 24 Zhu (10.1016/j.neucom.2019.05.109_bib0008) 2017; 28 Zhu (10.1016/j.neucom.2019.05.109_bib0006) 2009; 151 Suescun (10.1016/j.neucom.2019.05.109_bib0001) 1998; 70 Belchior (10.1016/j.neucom.2019.05.109_bib0032) 2012; 37 Qiao (10.1016/j.neucom.2019.05.109_bib0011) 2018; 275 Han (10.1016/j.neucom.2019.05.109_bib0012) 2012; 20 Punal (10.1016/j.neucom.2019.05.109_bib0005) 2001; 43 Yang (10.1016/j.neucom.2019.05.109_bib0017) 2015; 26 Han (10.1016/j.neucom.2019.05.109_bib0018) 2014; 128 Han (10.1016/j.neucom.2019.05.109_bib0010) 2011; 11 Alex (10.1016/j.neucom.2019.05.109_bib0021) 2018 Traor (10.1016/j.neucom.2019.05.109_bib0004) 2005; 111 Le (10.1016/j.neucom.2019.05.109_bib0029) 2017 Vilanova (10.1016/j.neucom.2019.05.109_bib0030) 2011; 3 |
| References_xml | – volume: 17 start-page: 1411 year: 2006 end-page: 1423 ident: bib0019 article-title: A fast and accurate online sequential learning algorithm for feedforward networks publication-title: IEEE Trans. Neural Netw. – volume: 70 start-page: 489 year: 2006 end-page: 501 ident: bib0023 article-title: Extreme learning machine: theory and applications publication-title: Neurocomputing – volume: 70 start-page: 316 year: 1998 end-page: 322 ident: bib0001 article-title: Dissolved oxygen control and simultaneous estimation of oxygen uptake rate in activated-sludge plants publication-title: Water Environ. Res. – volume: 24 start-page: 1425 year: 2013 end-page: 1436 ident: bib0013 article-title: Real-time model predictive control using a self-organizing neural network publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 43 start-page: 191 year: 2001 end-page: 198 ident: bib0005 article-title: Advanced monitoring and control of anaerobic wastewater treatment plants: diagnosis and supervision by a fuzzy-based expert system publication-title: Water Sci. Technol. – volume: 26 start-page: 3278 year: 2015 end-page: 3286 ident: bib0017 article-title: Robust integral of neural network and error sign control of mimo nonlinear systems publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 20 start-page: 465 year: 2012 end-page: 476 ident: bib0012 article-title: Model predictive control of dissolved oxygen concentration based on a self-organizing rbf neural network publication-title: Control Eng. Pract. – volume: 151 start-page: 195 year: 2009 end-page: 201 ident: bib0006 article-title: Optimization of anoxic/oxic step feeding activated sludge process with fuzzy control model for improving nitrogen removal publication-title: Chem. Eng. J. – volume: 128 start-page: 128 year: 2014 end-page: 135 ident: bib0018 article-title: Hierarchical extreme learning machine for feedforward neural network publication-title: Neurocomputing – volume: 111 start-page: 13 year: 2005 end-page: 19 ident: bib0004 article-title: Fuzzy control of dissolved oxygen in a sequencing batch reactor pilot plant publication-title: Chem. Eng. J. – volume: 45 start-page: 239 year: 2009 end-page: 248 ident: bib0002 article-title: Multivariable pid control design for activated sludge process with nitrification and denitrification publication-title: Biochem. Eng. J. – volume: 136 start-page: 88 year: 2014 end-page: 95 ident: bib0007 article-title: Fuzzy model-based predictive control of dissolved oxygen in activated sludge processes publication-title: Neurocomputing – volume: 3 start-page: 1 year: 2011 end-page: 11 ident: bib0030 article-title: N-Removal on wastewater treatment plants: a process control approach publication-title: J. Water Resource Prot. – volume: 61 start-page: 1970 year: 2013 end-page: 1982 ident: bib0014 article-title: Nonlinear model predictive control for industrial processes: an application to wastewater treatment process publication-title: IEEE Trans. Ind. Electron. – year: 2017 ident: bib0029 article-title: Gogp: fast online regression with gaussian processes publication-title: 2017 IEEE International Conference on Data Mining – volume: 11 start-page: 3812 year: 2011 end-page: 3820 ident: bib0010 article-title: Adaptive dissolved oxygen control based on dynamic structure neural networ publication-title: Appl. Soft Comput. – volume: 26 start-page: 775 year: 2015 end-page: 787 ident: bib0016 article-title: A data-based online reinforcement learning algorithm satisfying probably approximately correct principle publication-title: Neural Comput. Appl. – volume: 2 start-page: 985 year: 2004 end-page: 990 ident: bib0022 article-title: Extreme learning machine: a new learning scheme of feedforward neural networks publication-title: Proceedings of the 2004 IEEE International Joint Conference on Neural Networks, 2004. – year: 2015 ident: bib0033 article-title: Dissolved oxygen control system based on the t-s fuzzy neural network publication-title: International Joint Conference on Neural Networks – year: 2013 ident: bib0024 article-title: An Introduction to Optimization (four edition) – volume: 37 start-page: 152â162 year: 2012 ident: bib0032 article-title: Dissolved oxygen control of the activated sludge wastewater treatment process using stable adaptive fuzzy control publication-title: Comput. Chem. Eng. – volume: 64 start-page: 3936 year: 2019 end-page: 3942 ident: bib0037 article-title: Output-constrained control of nonaffine multiagent systems with partially unknown control directions publication-title: IEEE Trans. Automat. Contr. – year: 2007 ident: bib0028 publication-title: Online Support Vector Machines for Regression – volume: 2014 start-page: 1 year: 2014 end-page: 11 ident: bib0025 article-title: Adaptive control of nonlinear discrete-time systems by using os-elm neural networks publication-title: Abstr. Appl. Anal. – volume: 38 start-page: 209 year: 1998 end-page: 217 ident: bib0003 article-title: Energy saving in the aeration process by fuzzy logic control publication-title: Water Sci. Technol. – volume: 16 start-page: 1213 year: 2014 end-page: 1223 ident: bib0009 article-title: Neural network on-line modeling and controlling method for multi-variable control of wastewater treatment processes publication-title: Asian J. Control – volume: 275 start-page: 383 year: 2018 end-page: 393 ident: bib0011 article-title: Adaptive fuzzy neural network control of wastewater treatment process with multiobjective operation publication-title: Neurocomputing – start-page: 7 year: 2018 end-page: 8 ident: bib0021 article-title: Benchmark Simulation Model No.1 (Bsm1) – volume: 65 start-page: 125 year: 2016 end-page: 132 ident: bib0026 article-title: Adaptive control of nonlinear system using online error minimum neural network publication-title: ISA Trans. – volume: 68 start-page: 1516 year: 2017 end-page: 1524 ident: bib0027 article-title: Dissolved oxygen control of wastewater treatment process using self-organizing fuzzy neural network publication-title: CIESC J. – start-page: 123 year: 2001 end-page: 133 ident: bib0020 article-title: The Cost Simulation Benchmark Description and Simulator Manual – volume: 32 start-page: 1270 year: 2008 end-page: 1278 ident: bib0031 article-title: Dissolved oxygen control of the activated sludge wastewater treatment process using model predictive control publication-title: Comput. Chem. Eng. – volume: 28 start-page: 714 year: 2017 end-page: 725 ident: bib0008 article-title: Iterative adaptive dynamic programming for solving unknown nonlinear zero-sum game based on online data publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 27 start-page: 402 year: 2016 ident: bib0015 article-title: Nonlinear model predictive control based on a self-organizing recurrent neural network publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 42 start-page: 377 year: 2012 end-page: 390 ident: bib36 article-title: Reinforcement learning controller design for affine nonlinear discrete-time systems using online approximators publication-title: IEEE Trans. Syst., Man, Cybern. B, Cybern – year: 1984 ident: bib0034 article-title: Adaptive Filtering, Prediction and Control – volume: 45 start-page: 239 issue: 3 year: 2009 ident: 10.1016/j.neucom.2019.05.109_bib0002 article-title: Multivariable pid control design for activated sludge process with nitrification and denitrification publication-title: Biochem. Eng. J. doi: 10.1016/j.bej.2009.04.016 – volume: 136 start-page: 88 issue: 1 year: 2014 ident: 10.1016/j.neucom.2019.05.109_bib0007 article-title: Fuzzy model-based predictive control of dissolved oxygen in activated sludge processes publication-title: Neurocomputing doi: 10.1016/j.neucom.2014.01.025 – year: 1984 ident: 10.1016/j.neucom.2019.05.109_bib0034 – volume: 26 start-page: 3278 issue: 12 year: 2015 ident: 10.1016/j.neucom.2019.05.109_bib0017 article-title: Robust integral of neural network and error sign control of mimo nonlinear systems publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2015.2470175 – volume: 32 start-page: 1270 issue: 6 year: 2008 ident: 10.1016/j.neucom.2019.05.109_bib0031 article-title: Dissolved oxygen control of the activated sludge wastewater treatment process using model predictive control publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2007.06.008 – start-page: 123 year: 2001 ident: 10.1016/j.neucom.2019.05.109_bib0020 – volume: 42 start-page: 377 issue: 2 year: 2012 ident: 10.1016/j.neucom.2019.05.109_bib36 article-title: Reinforcement learning controller design for affine nonlinear discrete-time systems using online approximators publication-title: IEEE Trans. Syst., Man, Cybern. B, Cybern doi: 10.1109/TSMCB.2011.2166384 – volume: 70 start-page: 316 issue: 3 year: 1998 ident: 10.1016/j.neucom.2019.05.109_bib0001 article-title: Dissolved oxygen control and simultaneous estimation of oxygen uptake rate in activated-sludge plants publication-title: Water Environ. Res. doi: 10.2175/106143098X124948 – volume: 275 start-page: 383 year: 2018 ident: 10.1016/j.neucom.2019.05.109_bib0011 article-title: Adaptive fuzzy neural network control of wastewater treatment process with multiobjective operation publication-title: Neurocomputing doi: 10.1016/j.neucom.2017.08.059 – volume: 17 start-page: 1411 issue: 6 year: 2006 ident: 10.1016/j.neucom.2019.05.109_bib0019 article-title: A fast and accurate online sequential learning algorithm for feedforward networks publication-title: IEEE Trans. Neural Netw. doi: 10.1109/TNN.2006.880583 – year: 2013 ident: 10.1016/j.neucom.2019.05.109_bib0024 – volume: 3 start-page: 1 year: 2011 ident: 10.1016/j.neucom.2019.05.109_bib0030 article-title: N-Removal on wastewater treatment plants: a process control approach publication-title: J. Water Resource Prot. doi: 10.4236/jwarp.2011.31001 – volume: 37 start-page: 152â162 year: 2012 ident: 10.1016/j.neucom.2019.05.109_bib0032 article-title: Dissolved oxygen control of the activated sludge wastewater treatment process using stable adaptive fuzzy control publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2011.09.011 – volume: 38 start-page: 209 issue: 3 year: 1998 ident: 10.1016/j.neucom.2019.05.109_bib0003 article-title: Energy saving in the aeration process by fuzzy logic control publication-title: Water Sci. Technol. doi: 10.2166/wst.1998.0210 – volume: 43 start-page: 191 issue: 7 year: 2001 ident: 10.1016/j.neucom.2019.05.109_bib0005 article-title: Advanced monitoring and control of anaerobic wastewater treatment plants: diagnosis and supervision by a fuzzy-based expert system publication-title: Water Sci. Technol. doi: 10.2166/wst.2001.0422 – year: 2015 ident: 10.1016/j.neucom.2019.05.109_bib0033 article-title: Dissolved oxygen control system based on the t-s fuzzy neural network – volume: 65 start-page: 125 year: 2016 ident: 10.1016/j.neucom.2019.05.109_bib0026 article-title: Adaptive control of nonlinear system using online error minimum neural network publication-title: ISA Trans. doi: 10.1016/j.isatra.2016.07.012 – year: 2007 ident: 10.1016/j.neucom.2019.05.109_bib0028 – volume: 16 start-page: 1213 issue: 4 year: 2014 ident: 10.1016/j.neucom.2019.05.109_bib0009 article-title: Neural network on-line modeling and controlling method for multi-variable control of wastewater treatment processes publication-title: Asian J. Control doi: 10.1002/asjc.758 – volume: 128 start-page: 128 issue: 5 year: 2014 ident: 10.1016/j.neucom.2019.05.109_bib0018 article-title: Hierarchical extreme learning machine for feedforward neural network publication-title: Neurocomputing – volume: 11 start-page: 3812 issue: 4 year: 2011 ident: 10.1016/j.neucom.2019.05.109_bib0010 article-title: Adaptive dissolved oxygen control based on dynamic structure neural networ publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2011.02.014 – volume: 20 start-page: 465 issue: 4 year: 2012 ident: 10.1016/j.neucom.2019.05.109_bib0012 article-title: Model predictive control of dissolved oxygen concentration based on a self-organizing rbf neural network publication-title: Control Eng. Pract. doi: 10.1016/j.conengprac.2012.01.001 – volume: 70 start-page: 489 issue: 1 year: 2006 ident: 10.1016/j.neucom.2019.05.109_bib0023 article-title: Extreme learning machine: theory and applications publication-title: Neurocomputing doi: 10.1016/j.neucom.2005.12.126 – volume: 68 start-page: 1516 issue: 4 year: 2017 ident: 10.1016/j.neucom.2019.05.109_bib0027 article-title: Dissolved oxygen control of wastewater treatment process using self-organizing fuzzy neural network publication-title: CIESC J. – volume: 26 start-page: 775 issue: 4 year: 2015 ident: 10.1016/j.neucom.2019.05.109_bib0016 article-title: A data-based online reinforcement learning algorithm satisfying probably approximately correct principle publication-title: Neural Comput. Appl. doi: 10.1007/s00521-014-1738-2 – volume: 2014 start-page: 1 year: 2014 ident: 10.1016/j.neucom.2019.05.109_bib0025 article-title: Adaptive control of nonlinear discrete-time systems by using os-elm neural networks publication-title: Abstr. Appl. Anal. – year: 2017 ident: 10.1016/j.neucom.2019.05.109_bib0029 article-title: Gogp: fast online regression with gaussian processes – volume: 61 start-page: 1970 issue: 4 year: 2013 ident: 10.1016/j.neucom.2019.05.109_bib0014 article-title: Nonlinear model predictive control for industrial processes: an application to wastewater treatment process publication-title: IEEE Trans. Ind. Electron. doi: 10.1109/TIE.2013.2266086 – volume: 2 start-page: 985 year: 2004 ident: 10.1016/j.neucom.2019.05.109_bib0022 article-title: Extreme learning machine: a new learning scheme of feedforward neural networks – volume: 27 start-page: 402 issue: 2 year: 2016 ident: 10.1016/j.neucom.2019.05.109_bib0015 article-title: Nonlinear model predictive control based on a self-organizing recurrent neural network publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2015.2465174 – volume: 151 start-page: 195 issue: 1 year: 2009 ident: 10.1016/j.neucom.2019.05.109_bib0006 article-title: Optimization of anoxic/oxic step feeding activated sludge process with fuzzy control model for improving nitrogen removal publication-title: Chem. Eng. J. doi: 10.1016/j.cej.2009.02.019 – volume: 111 start-page: 13 issue: 1 year: 2005 ident: 10.1016/j.neucom.2019.05.109_bib0004 article-title: Fuzzy control of dissolved oxygen in a sequencing batch reactor pilot plant publication-title: Chem. Eng. J. doi: 10.1016/j.cej.2005.05.004 – volume: 24 start-page: 1425 issue: 9 year: 2013 ident: 10.1016/j.neucom.2019.05.109_bib0013 article-title: Real-time model predictive control using a self-organizing neural network publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2013.2261574 – volume: 64 start-page: 3936 issue: 9 year: 2019 ident: 10.1016/j.neucom.2019.05.109_bib0037 article-title: Output-constrained control of nonaffine multiagent systems with partially unknown control directions publication-title: IEEE Trans. Automat. Contr. doi: 10.1109/TAC.2019.2892391 – start-page: 7 year: 2018 ident: 10.1016/j.neucom.2019.05.109_bib0021 – volume: 28 start-page: 714 issue: 3 year: 2017 ident: 10.1016/j.neucom.2019.05.109_bib0008 article-title: Iterative adaptive dynamic programming for solving unknown nonlinear zero-sum game based on online data publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2016.2561300 |
| SSID | ssj0017129 |
| Score | 2.4997256 |
| Snippet | Wastewater Treatment Plant (WWTP) is challenging to regulate for its complex chemical and biological characteristics, and its precise mathematical model is... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 169 |
| SubjectTerms | Adaptive control Benchmark simulation model no.1 (BSM1) OS-ELM algorithm Wastewater treatment plant |
| Title | Online sequential extreme learning machine based adaptive control for wastewater treatment plant |
| URI | https://dx.doi.org/10.1016/j.neucom.2019.05.109 |
| Volume | 408 |
| WOSCitedRecordID | wos000571573400018&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1872-8286 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017129 issn: 0925-2312 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwELag5cCFN2rLQz5wWxkljp1JjquqCBCqQBSxt2A7DtqqG1bd7ePnM34lixbxkrhEkbVOLM-X8fjbzzOEvChtZ6uuaFlWyJIJEJoproGB5AjpSgtufRLXd3B8XM1m9ftIZa98OQHo--r6ul7-V1NjGxrbHZ39C3MPD8UGvEej4xXNjtc_MnxIHjoJGum1I8TR_zoWMFWI-DpZeAWlnbglrJ2oVi29gCjJ1p3y8EqtHK3mUiiOWvTlmep_4PJ9ag_jC0NEymG6cJkXWgezgWI4VJ6P_WznV3Y-uJlIVH-Y94uY_TuSD7jTjEqJxIhtnYoJ1CKXDOPG4GVtcKwVcH9kfdPziqza8J15qNkSl-E8FFTZ8vCBbDh92dsLJ_fB-KV2qVfzrB5XtEFn-NENxY2Eoy_D9bq8SXY5yBrd3-70zdHs7fCHE-Q8pGWMQ0-nLL0UcPtdP49iNiKTk3vkTtxS0GmAwn1yw_YPyN1UroNG7_2QfAnIoCMyaEQGTcigERnUI4MmZNCIDIrIoCMy6IAM6pHxiHx6dXRy-JrFAhvM4E5xzawp8pJnZdcCrnIY-0rN8RaMxBZlca9eaCOqTGe6BI2eHkwpoOW5VibDj794THb6b73dI9R0uoYaXGbZUkhQtRVSCeg6sK0EXuyTIk1YY2L2eVcE5axJMsPTJkxz46a5yaTTRuwTNvRahuwrv_k9JFs0MYIMkWGD8Pllz4N_7vmE3B6_jKdkZ31-YZ-RW-ZyPV-dP484-w5G1JrK |
| linkProvider | Elsevier |
| 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=Online+sequential+extreme+learning+machine+based+adaptive+control+for+wastewater+treatment+plant&rft.jtitle=Neurocomputing+%28Amsterdam%29&rft.au=Cao%2C+Weiwei&rft.au=Yang%2C+Qinmin&rft.date=2020-09-30&rft.pub=Elsevier+B.V&rft.issn=0925-2312&rft.eissn=1872-8286&rft.volume=408&rft.spage=169&rft.epage=175&rft_id=info:doi/10.1016%2Fj.neucom.2019.05.109&rft.externalDocID=S0925231220303416 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0925-2312&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0925-2312&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0925-2312&client=summon |