Temperature modeling of wave rotor refrigeration process based on elastic net variable selection and deep belief network
Optimal operation modeling plays an important role in wave rotor refrigeration process; however, considering covariance among multiple variables and high nonlinearity in wave rotor refrigeration process, it becomes more and more difficult to establish an accurate operation modeling using first-princ...
Uloženo v:
| Vydáno v: | Chemometrics and intelligent laboratory systems Ročník 239; s. 104872 |
|---|---|
| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier B.V
15.08.2023
|
| Témata: | |
| ISSN: | 0169-7439 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Optimal operation modeling plays an important role in wave rotor refrigeration process; however, considering covariance among multiple variables and high nonlinearity in wave rotor refrigeration process, it becomes more and more difficult to establish an accurate operation modeling using first-principles methods. This study proposed a novel modeling algorithm for the temperature parameter of the wave rotor refrigeration process based on elastic net and dingo optimization deep belief network (Enet-DOA-DBN). Firstly, to determine the correlation between the input variables and reduce the dimension of the input variables, the elastic net (Enet) algorithm is used to select the input variables that are irrelevant to the temperature parameter of the wave rotor refrigeration process. In this way, the covariance between multiple variables is eliminated and the model structure is simplified. Secondly, in order to improve the generalization of the temperature parameter model, a deep belief network (DBN) deep learning is proposed for modeling the temperature parameter of the wave rotor refrigeration process. Considering that the numerous hyperparameters of DBN algorithm have a great impact on the training and prediction results, the hyperparameters are optimized by the dingo optimization algorithm (DOA). The proposed Enet-DOA-DBN algorithm is validated by simulation using the benchmark data sets and the wave rotor refrigeration industrial process data sets. The simulation results show that the proposed Enet-DOA-DBN algorithm has good generalization ability, meanwhile it can effectively implement variable selection and simplify the model structure.
•A novel Enet-DOA-DBN algorithm based on elastic net variable selection method and DOA optimization DBN is proposed.•The Enet algorithm is used to select the input variables and the covariance between multiple variables is eliminated.•The Enet-DOA-DBN algorithm has good performance in simplifying the model structure.•Simulation results show that the Enet-DOA-DBN algorithm has good generalization capability. |
|---|---|
| AbstractList | Optimal operation modeling plays an important role in wave rotor refrigeration process; however, considering covariance among multiple variables and high nonlinearity in wave rotor refrigeration process, it becomes more and more difficult to establish an accurate operation modeling using first-principles methods. This study proposed a novel modeling algorithm for the temperature parameter of the wave rotor refrigeration process based on elastic net and dingo optimization deep belief network (Enet-DOA-DBN). Firstly, to determine the correlation between the input variables and reduce the dimension of the input variables, the elastic net (Enet) algorithm is used to select the input variables that are irrelevant to the temperature parameter of the wave rotor refrigeration process. In this way, the covariance between multiple variables is eliminated and the model structure is simplified. Secondly, in order to improve the generalization of the temperature parameter model, a deep belief network (DBN) deep learning is proposed for modeling the temperature parameter of the wave rotor refrigeration process. Considering that the numerous hyperparameters of DBN algorithm have a great impact on the training and prediction results, the hyperparameters are optimized by the dingo optimization algorithm (DOA). The proposed Enet-DOA-DBN algorithm is validated by simulation using the benchmark data sets and the wave rotor refrigeration industrial process data sets. The simulation results show that the proposed Enet-DOA-DBN algorithm has good generalization ability, meanwhile it can effectively implement variable selection and simplify the model structure.
•A novel Enet-DOA-DBN algorithm based on elastic net variable selection method and DOA optimization DBN is proposed.•The Enet algorithm is used to select the input variables and the covariance between multiple variables is eliminated.•The Enet-DOA-DBN algorithm has good performance in simplifying the model structure.•Simulation results show that the Enet-DOA-DBN algorithm has good generalization capability. |
| ArticleNumber | 104872 |
| Author | Qiao, Wenxu Shi, Yaru Hu, Xiaopeng Wang, Fan Li, Qi Ba, Wei |
| Author_xml | – sequence: 1 givenname: Qi orcidid: 0000-0002-0033-6204 surname: Li fullname: Li, Qi email: qili@dlut.edu.cn organization: Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116023, China – sequence: 2 givenname: Wenxu surname: Qiao fullname: Qiao, Wenxu organization: Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116023, China – sequence: 3 givenname: Yaru surname: Shi fullname: Shi, Yaru organization: Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116023, China – sequence: 4 givenname: Wei surname: Ba fullname: Ba, Wei organization: College of Electrical and Information Engineering, Dalian Jiaotong University, Dalian, 116023, China – sequence: 5 givenname: Fan surname: Wang fullname: Wang, Fan organization: Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116023, China – sequence: 6 givenname: Xiaopeng surname: Hu fullname: Hu, Xiaopeng organization: Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116023, China |
| BookMark | eNqFkMtOwzAQRb0oEi3wC8g_kGI7r0ZiAap4SZXYlLXlx7i4JHZkhxb-HqeFDZuuRjNz75XumaGJ8w4QuqZkTgmtbrZz9Q6db4WcM8LydCwWNZugaXo2WV3kzTmaxbgl417QKfpaQ9dDEMNnANx5Da11G-wN3osd4OAHH3AAE-xmFFnvcB-8ghixFBE0TgdoRRyswg4GvBPBCtkCjtCCOuiF01gD9FimbDCjbO_DxyU6M6KNcPU7L9Db48N6-ZytXp9elverTOWUDZkpKBF53chSUmrqSjakLgUFqcqiBE3qmiqtS1UlRaWaRQMi15rlzDBDGavyC1Qdc1XwMaYqvA-2E-GbU8JHZnzL_5jxkRk_MkvG239GZYcDgiEI25623x3tkMrtLAQelQWnQNuQyHDt7amIH-YBk7Y |
| CitedBy_id | crossref_primary_10_1088_1361_6501_ad8d70 crossref_primary_10_1016_j_jfranklin_2024_106806 crossref_primary_10_1016_j_jobe_2023_107227 crossref_primary_10_1016_j_meaene_2025_100061 |
| Cites_doi | 10.1155/2021/9107547 10.1111/j.1467-9868.2005.00503.x 10.1016/j.jngse.2016.06.054 10.1007/s10462-021-10038-8 10.1016/j.chemolab.2022.104617 10.1016/j.chemolab.2013.04.006 10.1016/j.istruc.2021.05.096 10.1016/j.chemolab.2019.03.008 10.1016/j.chemolab.2022.104624 10.1016/j.applthermaleng.2022.118893 10.1016/j.jsv.2017.03.025 10.1016/j.ijrefrig.2014.10.017 10.1115/1.4051758 10.1016/j.foodcont.2021.107967 10.1162/neco.2006.18.7.1527 10.1016/j.autcon.2022.104488 10.1016/j.chemolab.2022.104685 10.1162/NECO_a_00311 10.1016/j.compstruc.2021.106653 10.1016/j.asoc.2022.108692 10.1016/j.cherd.2019.06.034 10.1016/j.energy.2022.123350 10.1016/j.ijrefrig.2020.12.015 10.3182/20140824-6-ZA-1003.00204 10.1016/j.asr.2022.01.043 10.1016/j.chemolab.2020.104123 |
| ContentType | Journal Article |
| Copyright | 2023 Elsevier B.V. |
| Copyright_xml | – notice: 2023 Elsevier B.V. |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.chemolab.2023.104872 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Chemistry |
| ExternalDocumentID | 10_1016_j_chemolab_2023_104872 S0169743923001223 |
| GroupedDBID | --- --K --M .DC .~1 0R~ 1B1 1RT 1~. 1~5 29B 4.4 457 4G. 53G 5GY 5VS 6J9 7-5 71M 8P~ 9JN AACTN AAEDT AAEDW AAIKJ AAKOC AALRI AAOAW AAQFI AARLI AAXKI AAXUO ABAOU ABFRF ABJNI ABMAC ACDAQ ACGFO ACGFS ACRLP ADBBV ADECG ADEZE ADGUI AEBSH AEFWE AEKER AENEX AFJKZ AFKWA AFTJW AFZHZ AGHFR AGUBO AGYEJ AHHHB AIEXJ AIGVJ AIKHN AITUG AJOXV AJSZI AKRWK ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ ARUGR AXJTR BKOJK BLXMC CS3 DU5 EBS EFJIC EO8 EO9 EP2 EP3 FDB FIRID FLBIZ FNPLU FYGXN G-Q GBLVA IHE J1W KOM M36 M41 MHUIS MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 RNS ROL RPZ SCH SDF SDG SDP SES SPC SPCBC SSK SSW SSZ T5K UNMZH YK3 ~02 ~G- 9DU AAQXK AATTM AAYWO AAYXX ABFNM ABWVN ABXDB ACLOT ACNNM ACRPL ACVFH ADCNI ADMUD ADNMO AEIPS AEUPX AFFNX AFPUW AGQPQ AIGII AIIUN AJQLL AKBMS AKYEP ANKPU APXCP ASPBG AVWKF AZFZN CITATION EFKBS EFLBG EJD FEDTE FGOYB HMU HVGLF HZ~ R2- SCB SEW WUQ XPP ~HD |
| ID | FETCH-LOGICAL-c312t-f410a379b5b11f76b9075a1ebc545ed0771cdd5c69b56c989ea3dd232f2f12263 |
| ISICitedReferencesCount | 5 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001010323100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0169-7439 |
| IngestDate | Sat Nov 29 07:27:05 EST 2025 Tue Nov 18 21:09:04 EST 2025 Tue Dec 03 03:44:36 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Elastic net Wave rotor refrigeration process Deep belief network Dingo optimization algorithm Variable selection |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c312t-f410a379b5b11f76b9075a1ebc545ed0771cdd5c69b56c989ea3dd232f2f12263 |
| ORCID | 0000-0002-0033-6204 |
| ParticipantIDs | crossref_primary_10_1016_j_chemolab_2023_104872 crossref_citationtrail_10_1016_j_chemolab_2023_104872 elsevier_sciencedirect_doi_10_1016_j_chemolab_2023_104872 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-08-15 |
| PublicationDateYYYYMMDD | 2023-08-15 |
| PublicationDate_xml | – month: 08 year: 2023 text: 2023-08-15 day: 15 |
| PublicationDecade | 2020 |
| PublicationTitle | Chemometrics and intelligent laboratory systems |
| PublicationYear | 2023 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Liu, Qi (bib24) 2017; 12 Balaji, Lavanya, Mary (bib16) 2020; 204 Wang, Aman, Liu, Guan, Yuan, Wang (bib18) 2022; 228 Gu, Yang, Zhou (bib30) 2022; 153 Kocyigit (bib6) 2015; 50 Hinton, Osindero, Teh (bib8) 2006; 18 Ge, Gao, Cui, Chen, Wang (bib12) 2022; 142 Wu, Zhang, Zheng, Liu, Lundteigen (bib9) 2016; 34 Chen, Zhang (bib10) 2022; 121 Li, Wang, Chen, Han (bib14) 2022; 69 Peraza-Vázquez, Peña-Delgado, Echavarría-Castillo (bib28) 2021; 2021 Liu, Li, Liu, Feng, Yu, Hu, Dao (bib1) 2021; 124 Dirks, Poole (bib19) 2022; 231 Pfeiffer, Ronai, Vorlaufer, Dörr, Filzmoser (bib25) 2022; 228 Chung, Gu, Yoo (bib7) 2022; 246 Wang, Jia, Zhou (bib5) 2022; 55 Lyu, Chen, Song (bib11) 2019; 189 Zou, Hastie (bib27) 2005; 67 Yu, Yang, Ding, Wang, Wang (bib17) 2021; 257 Herceg, Andrijić, Bolf (bib3) 2019; 149 Reddy, Av (bib15) 2022; 77 Chiu, Yao (bib26) 2013; 125 Fang, Roy, Mares, Sham, Chen, Lim (bib13) 2021; 33 Liu, Feng, Liu, Wang, Hu (bib2) 2022; 144 Ling, Dai, Xing, Tong (bib20) 2021; 44 Gauthier, Scullion, Berry (bib23) 2017; 400 Salakhutdinov, Hinton (bib29) 2012; 24 Fonseca, Duarte, Oliveira, Machado, Maia (bib4) 2022; 214 Ribeiro, Salva, Silvarolla (bib21) 2021; 125 Yan, Chiu, Dong, Yao (bib22) 2014; 47 Wu (10.1016/j.chemolab.2023.104872_bib9) 2016; 34 Fonseca (10.1016/j.chemolab.2023.104872_bib4) 2022; 214 Liu (10.1016/j.chemolab.2023.104872_bib2) 2022; 144 Ling (10.1016/j.chemolab.2023.104872_bib20) 2021; 44 Herceg (10.1016/j.chemolab.2023.104872_bib3) 2019; 149 Ribeiro (10.1016/j.chemolab.2023.104872_bib21) 2021; 125 Wang (10.1016/j.chemolab.2023.104872_bib5) 2022; 55 Gauthier (10.1016/j.chemolab.2023.104872_bib23) 2017; 400 Salakhutdinov (10.1016/j.chemolab.2023.104872_bib29) 2012; 24 Chung (10.1016/j.chemolab.2023.104872_bib7) 2022; 246 Balaji (10.1016/j.chemolab.2023.104872_bib16) 2020; 204 Peraza-Vázquez (10.1016/j.chemolab.2023.104872_bib28) 2021; 2021 Hinton (10.1016/j.chemolab.2023.104872_bib8) 2006; 18 Kocyigit (10.1016/j.chemolab.2023.104872_bib6) 2015; 50 Chiu (10.1016/j.chemolab.2023.104872_bib26) 2013; 125 Chen (10.1016/j.chemolab.2023.104872_bib10) 2022; 121 Reddy (10.1016/j.chemolab.2023.104872_bib15) 2022; 77 Liu (10.1016/j.chemolab.2023.104872_bib1) 2021; 124 Wang (10.1016/j.chemolab.2023.104872_bib18) 2022; 228 Gu (10.1016/j.chemolab.2023.104872_bib30) 2022; 153 Fang (10.1016/j.chemolab.2023.104872_bib13) 2021; 33 Yu (10.1016/j.chemolab.2023.104872_bib17) 2021; 257 Li (10.1016/j.chemolab.2023.104872_bib14) 2022; 69 Dirks (10.1016/j.chemolab.2023.104872_bib19) 2022; 231 Yan (10.1016/j.chemolab.2023.104872_bib22) 2014; 47 Lyu (10.1016/j.chemolab.2023.104872_bib11) 2019; 189 Pfeiffer (10.1016/j.chemolab.2023.104872_bib25) 2022; 228 Zou (10.1016/j.chemolab.2023.104872_bib27) 2005; 67 Liu (10.1016/j.chemolab.2023.104872_bib24) 2017; 12 Ge (10.1016/j.chemolab.2023.104872_bib12) 2022; 142 |
| References_xml | – volume: 144 year: 2022 ident: bib2 article-title: Performance analysis of wave rotor based on response surface optimization method publication-title: J. Energy Resour. Technol. – volume: 400 start-page: 134 year: 2017 end-page: 153 ident: bib23 article-title: Sound quality prediction based on systematic metric selection and shrinkage: comparison of stepwise, lasso, and elastic-net algorithms and clustering preprocessing publication-title: J. Sound Vib. – volume: 204 year: 2020 ident: bib16 article-title: Clustering of mixed datasets using deep learning algorithm publication-title: Chemometr. Intell. Lab. Syst. – volume: 189 start-page: 8 year: 2019 end-page: 17 ident: bib11 article-title: Image-based process monitoring using deep learning framework publication-title: Chemometr. Intell. Lab. Syst. – volume: 231 year: 2022 ident: bib19 article-title: Automatic neural network hyperparameter optimization for extrapolation: lessons learned from visible and near-infrared spectroscopy of mango fruit publication-title: Chemometr. Intell. Lab. Syst. – volume: 153 start-page: 49 year: 2022 end-page: 63 ident: bib30 article-title: Approximation properties of Gaussian-binary restricted Boltzmann machines and Gaussian-binary deep belief networks, Neural publication-title: Netw – volume: 228 year: 2022 ident: bib25 article-title: Weighted LASSO variable selection for the analysis of FTIR spectra applied to the prediction of engine oil degradation publication-title: Chemometr. Intell. Lab. Syst. – volume: 214 year: 2022 ident: bib4 article-title: Mass flow prediction in a refrigeration machine using artificial neural networks publication-title: Appl. Therm. Eng. – volume: 69 start-page: 3071 year: 2022 end-page: 3087 ident: bib14 article-title: Spatiotemporal assessment of landslide susceptibility in Southern Sichuan, China using SA-DBN, PSO-DBN and SSA-DBN models compared with DBN model publication-title: Adv. Space Res. – volume: 18 start-page: 1527 year: 2006 end-page: 1554 ident: bib8 article-title: A fast learning algorithm for deep belief nets publication-title: Neural Comput. – volume: 125 year: 2021 ident: bib21 article-title: Prediction of a wide range of compounds concentration in raw coffee beans using NIRS, PLS and variable selection publication-title: Food Control – volume: 24 start-page: 1967 year: 2012 end-page: 2006 ident: bib29 article-title: An efficient learning procedure for deep Boltzmann machines publication-title: Neural Comput. – volume: 2021 year: 2021 ident: bib28 article-title: A bio-inspired method for engineering design optimization inspired by dingoes hunting strategies publication-title: Math. Probl Eng. – volume: 228 year: 2022 ident: bib18 article-title: Inter-Relational Mahalanobis SAE with semi-supervised strategy for fault classification in chemical processes publication-title: Chemometr. Intell. Lab. Syst. – volume: 33 start-page: 2792 year: 2021 end-page: 2802 ident: bib13 article-title: Deep learning-based axial capacity prediction for cold-formed steel channel sections using Deep Belief Network publication-title: Structures – volume: 124 start-page: 96 year: 2021 end-page: 104 ident: bib1 article-title: Investigation on non-equilibrium phase transition in wave rotor publication-title: Int. J. Refrig. – volume: 142 year: 2022 ident: bib12 article-title: Safety prediction of shield tunnel construction using deep belief network and whale optimization algorithm publication-title: Autom. ConStruct. – volume: 50 start-page: 69 year: 2015 end-page: 79 ident: bib6 article-title: Fault and sensor error diagnostic strategies for a vapor compression refrigeration system by using fuzzy inference systems and artificial neural network publication-title: Int. J. Refrig. – volume: 121 year: 2022 ident: bib10 article-title: Applying Artificial Intelligence and Deep Belief Network to predict traffic congestion evacuation performance in smart cities publication-title: Appl. Soft Comput. – volume: 47 start-page: 6704 year: 2014 end-page: 6709 ident: bib22 article-title: A LASSO-based batch process modeling and end-product quality prediction method publication-title: IFAC Proc. Vol. – volume: 12 year: 2017 ident: bib24 article-title: An efficient elastic net with regression coefficients method for variable selection of spectrum data publication-title: PLoS One – volume: 149 start-page: 95 year: 2019 end-page: 103 ident: bib3 article-title: Development of soft sensors for isomerization process based on support vector machine regression and dynamic polynomial models publication-title: Chem. Eng. Res. Des. – volume: 77 year: 2022 ident: bib15 article-title: Multi-channel neuro signal classification using Adam-based coyote optimization enabled deep belief network publication-title: Biomed. Signal Process Control – volume: 246 year: 2022 ident: bib7 article-title: District heater load forecasting based on machine learning and parallel CNN-LSTM attention publication-title: Energy – volume: 44 year: 2021 ident: bib20 article-title: An improved input variable selection method of the data-driven model for building heating load prediction publication-title: J. Build. Eng. – volume: 55 start-page: 565 year: 2022 end-page: 587 ident: bib5 article-title: Artificial neural networks for water quality soft-sensing in wastewater treatment: a review publication-title: Artif. Intell. Rev. – volume: 257 year: 2021 ident: bib17 article-title: Six sigma robust optimization method based on a pseudo single-loop strategy and RFR-DBN with insufficient samples publication-title: Comput. Struct. – volume: 67 start-page: 301 year: 2005 end-page: 320 ident: bib27 article-title: Regularization and variable selection via the elastic net publication-title: J. Roy. Stat. Soc. B – volume: 125 start-page: 153 year: 2013 end-page: 165 ident: bib26 article-title: Multiway elastic net (MEN) for final product quality prediction and quality-related analysis of batch processes publication-title: Chemometr. Intell. Lab. Syst. – volume: 34 start-page: 139 year: 2016 end-page: 158 ident: bib9 article-title: A DBN-based risk assessment model for prediction and diagnosis of offshore drilling incidents publication-title: J. Nat. Gas Sci. Eng. – volume: 2021 year: 2021 ident: 10.1016/j.chemolab.2023.104872_bib28 article-title: A bio-inspired method for engineering design optimization inspired by dingoes hunting strategies publication-title: Math. Probl Eng. doi: 10.1155/2021/9107547 – volume: 12 year: 2017 ident: 10.1016/j.chemolab.2023.104872_bib24 article-title: An efficient elastic net with regression coefficients method for variable selection of spectrum data publication-title: PLoS One – volume: 67 start-page: 301 year: 2005 ident: 10.1016/j.chemolab.2023.104872_bib27 article-title: Regularization and variable selection via the elastic net publication-title: J. Roy. Stat. Soc. B doi: 10.1111/j.1467-9868.2005.00503.x – volume: 34 start-page: 139 year: 2016 ident: 10.1016/j.chemolab.2023.104872_bib9 article-title: A DBN-based risk assessment model for prediction and diagnosis of offshore drilling incidents publication-title: J. Nat. Gas Sci. Eng. doi: 10.1016/j.jngse.2016.06.054 – volume: 55 start-page: 565 year: 2022 ident: 10.1016/j.chemolab.2023.104872_bib5 article-title: Artificial neural networks for water quality soft-sensing in wastewater treatment: a review publication-title: Artif. Intell. Rev. doi: 10.1007/s10462-021-10038-8 – volume: 153 start-page: 49 year: 2022 ident: 10.1016/j.chemolab.2023.104872_bib30 article-title: Approximation properties of Gaussian-binary restricted Boltzmann machines and Gaussian-binary deep belief networks, Neural publication-title: Netw – volume: 228 year: 2022 ident: 10.1016/j.chemolab.2023.104872_bib25 article-title: Weighted LASSO variable selection for the analysis of FTIR spectra applied to the prediction of engine oil degradation publication-title: Chemometr. Intell. Lab. Syst. doi: 10.1016/j.chemolab.2022.104617 – volume: 125 start-page: 153 year: 2013 ident: 10.1016/j.chemolab.2023.104872_bib26 article-title: Multiway elastic net (MEN) for final product quality prediction and quality-related analysis of batch processes publication-title: Chemometr. Intell. Lab. Syst. doi: 10.1016/j.chemolab.2013.04.006 – volume: 33 start-page: 2792 year: 2021 ident: 10.1016/j.chemolab.2023.104872_bib13 article-title: Deep learning-based axial capacity prediction for cold-formed steel channel sections using Deep Belief Network publication-title: Structures doi: 10.1016/j.istruc.2021.05.096 – volume: 189 start-page: 8 year: 2019 ident: 10.1016/j.chemolab.2023.104872_bib11 article-title: Image-based process monitoring using deep learning framework publication-title: Chemometr. Intell. Lab. Syst. doi: 10.1016/j.chemolab.2019.03.008 – volume: 228 year: 2022 ident: 10.1016/j.chemolab.2023.104872_bib18 article-title: Inter-Relational Mahalanobis SAE with semi-supervised strategy for fault classification in chemical processes publication-title: Chemometr. Intell. Lab. Syst. doi: 10.1016/j.chemolab.2022.104624 – volume: 214 year: 2022 ident: 10.1016/j.chemolab.2023.104872_bib4 article-title: Mass flow prediction in a refrigeration machine using artificial neural networks publication-title: Appl. Therm. Eng. doi: 10.1016/j.applthermaleng.2022.118893 – volume: 400 start-page: 134 year: 2017 ident: 10.1016/j.chemolab.2023.104872_bib23 article-title: Sound quality prediction based on systematic metric selection and shrinkage: comparison of stepwise, lasso, and elastic-net algorithms and clustering preprocessing publication-title: J. Sound Vib. doi: 10.1016/j.jsv.2017.03.025 – volume: 50 start-page: 69 year: 2015 ident: 10.1016/j.chemolab.2023.104872_bib6 article-title: Fault and sensor error diagnostic strategies for a vapor compression refrigeration system by using fuzzy inference systems and artificial neural network publication-title: Int. J. Refrig. doi: 10.1016/j.ijrefrig.2014.10.017 – volume: 77 year: 2022 ident: 10.1016/j.chemolab.2023.104872_bib15 article-title: Multi-channel neuro signal classification using Adam-based coyote optimization enabled deep belief network publication-title: Biomed. Signal Process Control – volume: 144 year: 2022 ident: 10.1016/j.chemolab.2023.104872_bib2 article-title: Performance analysis of wave rotor based on response surface optimization method publication-title: J. Energy Resour. Technol. doi: 10.1115/1.4051758 – volume: 125 year: 2021 ident: 10.1016/j.chemolab.2023.104872_bib21 article-title: Prediction of a wide range of compounds concentration in raw coffee beans using NIRS, PLS and variable selection publication-title: Food Control doi: 10.1016/j.foodcont.2021.107967 – volume: 18 start-page: 1527 year: 2006 ident: 10.1016/j.chemolab.2023.104872_bib8 article-title: A fast learning algorithm for deep belief nets publication-title: Neural Comput. doi: 10.1162/neco.2006.18.7.1527 – volume: 142 year: 2022 ident: 10.1016/j.chemolab.2023.104872_bib12 article-title: Safety prediction of shield tunnel construction using deep belief network and whale optimization algorithm publication-title: Autom. ConStruct. doi: 10.1016/j.autcon.2022.104488 – volume: 231 year: 2022 ident: 10.1016/j.chemolab.2023.104872_bib19 article-title: Automatic neural network hyperparameter optimization for extrapolation: lessons learned from visible and near-infrared spectroscopy of mango fruit publication-title: Chemometr. Intell. Lab. Syst. doi: 10.1016/j.chemolab.2022.104685 – volume: 24 start-page: 1967 year: 2012 ident: 10.1016/j.chemolab.2023.104872_bib29 article-title: An efficient learning procedure for deep Boltzmann machines publication-title: Neural Comput. doi: 10.1162/NECO_a_00311 – volume: 257 year: 2021 ident: 10.1016/j.chemolab.2023.104872_bib17 article-title: Six sigma robust optimization method based on a pseudo single-loop strategy and RFR-DBN with insufficient samples publication-title: Comput. Struct. doi: 10.1016/j.compstruc.2021.106653 – volume: 121 year: 2022 ident: 10.1016/j.chemolab.2023.104872_bib10 article-title: Applying Artificial Intelligence and Deep Belief Network to predict traffic congestion evacuation performance in smart cities publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2022.108692 – volume: 149 start-page: 95 year: 2019 ident: 10.1016/j.chemolab.2023.104872_bib3 article-title: Development of soft sensors for isomerization process based on support vector machine regression and dynamic polynomial models publication-title: Chem. Eng. Res. Des. doi: 10.1016/j.cherd.2019.06.034 – volume: 246 year: 2022 ident: 10.1016/j.chemolab.2023.104872_bib7 article-title: District heater load forecasting based on machine learning and parallel CNN-LSTM attention publication-title: Energy doi: 10.1016/j.energy.2022.123350 – volume: 44 year: 2021 ident: 10.1016/j.chemolab.2023.104872_bib20 article-title: An improved input variable selection method of the data-driven model for building heating load prediction publication-title: J. Build. Eng. – volume: 124 start-page: 96 year: 2021 ident: 10.1016/j.chemolab.2023.104872_bib1 article-title: Investigation on non-equilibrium phase transition in wave rotor publication-title: Int. J. Refrig. doi: 10.1016/j.ijrefrig.2020.12.015 – volume: 47 start-page: 6704 year: 2014 ident: 10.1016/j.chemolab.2023.104872_bib22 article-title: A LASSO-based batch process modeling and end-product quality prediction method publication-title: IFAC Proc. Vol. doi: 10.3182/20140824-6-ZA-1003.00204 – volume: 69 start-page: 3071 year: 2022 ident: 10.1016/j.chemolab.2023.104872_bib14 article-title: Spatiotemporal assessment of landslide susceptibility in Southern Sichuan, China using SA-DBN, PSO-DBN and SSA-DBN models compared with DBN model publication-title: Adv. Space Res. doi: 10.1016/j.asr.2022.01.043 – volume: 204 year: 2020 ident: 10.1016/j.chemolab.2023.104872_bib16 article-title: Clustering of mixed datasets using deep learning algorithm publication-title: Chemometr. Intell. Lab. Syst. doi: 10.1016/j.chemolab.2020.104123 |
| SSID | ssj0016941 |
| Score | 2.4221823 |
| Snippet | Optimal operation modeling plays an important role in wave rotor refrigeration process; however, considering covariance among multiple variables and high... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 104872 |
| SubjectTerms | Deep belief network Dingo optimization algorithm Elastic net Variable selection Wave rotor refrigeration process |
| Title | Temperature modeling of wave rotor refrigeration process based on elastic net variable selection and deep belief network |
| URI | https://dx.doi.org/10.1016/j.chemolab.2023.104872 |
| Volume | 239 |
| WOSCitedRecordID | wos001010323100001&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 issn: 0169-7439 databaseCode: AIEXJ dateStart: 19950101 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.sciencedirect.com omitProxy: false ssIdentifier: ssj0016941 providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwELaWLRJcEE9RXvKBW5QliddJfCxVEXCoQCpiOUV27EipSna1my77c_ipzMSPplBReuASZR3biXc-2zPjeRDy2ig5Z7BTgZia6HgO0w2mVMZioxINO1BSZ1oNySaK4-NysRCfJpOf3hdme1Z0XbnbidV_JTWUAbHRdfYG5A6dQgHcA9HhCmSH678R3gAnbCMl2zw3zq75B-YZWi9Bxo5gVwSh3Djir6yvQIQbmsbDAwMcNcZx7UwfbUGWHryrNkPCHG-8rI1BGy9gYBushtZdYzYXwxAsv2OyLhcDug2RP_vI4Q4P9zejeOloFTSYFnxugza2lYMm96vpdudBFzSkIY6-yXUoeittrXaswwAoYExZPlZr5iJG0Wi8Lmfup11ZQWwsbZKfPxZ9q384ndU4NhjDDF8xu2hwOcr2b7tfsEn05m6nle-nwn4q288tspcVXJRTsnfw4WjxMZxUoSOwjR9vRzDyQr_6i65mgEZMzcl9cs9JI_TAougBmZjuIblz6JMAPiK7EZqoRxNdNhTRRAc00Utoog5NdEAThQKHJgowoR5NNKCJAjoooolaNFGHpsfky7ujk8P3sUvWEdcszfq4maeJZIVQXKVpU-RKADMqU6Nq4NGNTooirbXmdQ418lqUwkimNfDzTdakIAOwJ2TaLTvzlFDZGM6ZZjk8njMDdROuCq7yzEheSLVPuP8Hq9pFsseEKmfV32m4T96Edisby-XaFsITqHIcqeU0K8DeNW2f3fhtz8ndi8nxgkz79bl5SW7X277drF854P0C-VC11w |
| 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=Temperature+modeling+of+wave+rotor+refrigeration+process+based+on+elastic+net+variable+selection+and+deep+belief+network&rft.jtitle=Chemometrics+and+intelligent+laboratory+systems&rft.au=Li%2C+Qi&rft.au=Qiao%2C+Wenxu&rft.au=Shi%2C+Yaru&rft.au=Ba%2C+Wei&rft.date=2023-08-15&rft.issn=0169-7439&rft.volume=239&rft.spage=104872&rft_id=info:doi/10.1016%2Fj.chemolab.2023.104872&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_chemolab_2023_104872 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0169-7439&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0169-7439&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0169-7439&client=summon |