{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T00:08:39Z","timestamp":1755907719720,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":10,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,11,18]],"date-time":"2023-11-18T00:00:00Z","timestamp":1700265600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,11,18]]},"DOI":"10.1145\/3603273.3635671","type":"proceedings-article","created":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T18:12:40Z","timestamp":1704823960000},"page":"96-100","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Parameter Optimization of Direct Speed Model Predict Control of Vehicle PMSM based on LSTM Network"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5143-9801","authenticated-orcid":false,"given":"Lixiao","family":"Gao","sequence":"first","affiliation":[{"name":"School of Electrical Engineering and Automation, Harbin Institute of Technology, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4402-3362","authenticated-orcid":false,"given":"Feng","family":"Chai","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Automation, Harbin Institute of Technology, China"}]}],"member":"320","published-online":{"date-parts":[[2024,1,9]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPEL.2019.2962857"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2897074"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TEC.2016.2559940"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"crossref","unstructured":"Yang L Wang F Zhang J 2019 Remaining useful life prediction of ultrasonic motor based on Elman neural network with improved particle swarm optimization Measurement 143 27-38.","DOI":"10.1016\/j.measurement.2019.05.013"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Obed A A Saleh A L and Kadhim A K 2019 Speed performance evaluation of BLDC motor based on dynamic wavelet neural network and PSO algorithm International Journal of Power Electronics and Drive System (IJPEDS) 10 1742-1750.","DOI":"10.11591\/ijpeds.v10.i4.pp1742-1750"},{"key":"e_1_3_2_1_6_1","first-page":"183","article-title":"Admixed recurrent Gegenbauer polynomials neural network with mended particle swarm optimization control system for synchronous reluctance motor driving continuously variable transmission system Proceedings of the Institution of Mechanical Engineers","volume":"234","author":"Lin C H","year":"2020","unstructured":"Lin C H and Chang K T 2020 Admixed recurrent Gegenbauer polynomials neural network with mended particle swarm optimization control system for synchronous reluctance motor driving continuously variable transmission system Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 234 183-198.","journal-title":"Part I: Journal of Systems and Control Engineering"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Gordon D Norouzi A Blomeyer G 2023 Support vector machine based emissions modeling using particle swarm optimization for homogeneous charge compression ignition engine International Journal of Engine Research 24 536-551.","DOI":"10.1177\/14680874211055546"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Su G Wang P Guo Y 2022 Multiparameter identification of permanent magnet synchronous motor based on model reference adaptive system\u2014Simulated annealing particle swarm optimization algorithm Electronics 11 159.","DOI":"10.3390\/electronics11010159"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","unstructured":"H\u00e9tu S Gr\u00e9goire M Saimpont A 2013 The neural network of motor imagery: an ALE meta-analysis Neuroscience & Biobehavioral Reviews 37 930-949.","DOI":"10.1016\/j.neubiorev.2013.03.017"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","unstructured":"Zhang K Robinson N Lee S W 2021 Adaptive transfer learning for EEG motor imagery classification with deep convolutional neural network Neural Networks 136 1-10.","DOI":"10.1016\/j.neunet.2020.12.013"}],"event":{"name":"AAIA 2023: 2023 International Conference on Advances in Artificial Intelligence and Applications","acronym":"AAIA 2023","location":"Wuhan China"},"container-title":["Proceedings of the 2023 International Conference on Advances in Artificial Intelligence and Applications"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3603273.3635671","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3603273.3635671","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T19:33:14Z","timestamp":1755891194000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3603273.3635671"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,18]]},"references-count":10,"alternative-id":["10.1145\/3603273.3635671","10.1145\/3603273"],"URL":"https:\/\/doi.org\/10.1145\/3603273.3635671","relation":{},"subject":[],"published":{"date-parts":[[2023,11,18]]},"assertion":[{"value":"2024-01-09","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}