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The adaptive particle swarm optimization algorithm completes the operation by continuously updating the particles in the spatial domain; after discussing its application principle in detail, the convergence effect is more optimal; and the algorithms are applied to parameter optimization for support vector regression models. After employing the adaptive particle swarm optimization algorithm, the evaluation indicators and experimental prediction results demonstrate that the APSO model has fewer errors, a higher tracking degree, superior generalization performance, and greater prediction accuracy. This is a useful resource for forecasting grain temperature trends.<\/jats:p>","DOI":"10.3233\/jcm-226642","type":"journal-article","created":{"date-parts":[[2023,2,14]],"date-time":"2023-02-14T10:44:32Z","timestamp":1676371472000},"page":"1547-1559","source":"Crossref","is-referenced-by-count":1,"title":["Research on grain-stored temperature prediction model based on improved SVR algorithm"],"prefix":"10.66113","volume":"23","author":[{"given":"Zhihui","family":"Li","sequence":"first","affiliation":[{"name":"Key Laboratory of Grain Information Processing and Control (Henan University of Technology), Ministry of Education"},{"name":"College of Information Science and Engineering, Henan University of Technology, Zhengzhou, Henan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiyi","family":"Si","sequence":"additional","affiliation":[{"name":"Key Laboratory of Grain Information Processing and Control (Henan University of Technology), Ministry of Education"},{"name":"College of Information Science and Engineering, Henan University of Technology, Zhengzhou, Henan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhua","family":"Zhu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Grain Information Processing and Control (Henan University of Technology), Ministry of Education"},{"name":"College of Information Science and Engineering, Henan University of Technology, Zhengzhou, Henan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"55691","reference":[{"issue":"14","key":"10.3233\/JCM-226642_ref1","first-page":"918","article-title":"Prediction of grain storage temperature based on deep learning","volume":"32","author":"Li","year":"2017","journal-title":"Revista de la Facultad de Ingenieria."},{"issue":"4","key":"10.3233\/JCM-226642_ref2","first-page":"99","article-title":"Research on grain storage temperature prediction model based on time series method","volume":"30","author":"Li","year":"2018","journal-title":"IPPTA: Quarterly Journal of Indian Pulp and Paper Technical Association."},{"issue":"4","key":"10.3233\/JCM-226642_ref3","first-page":"663","article-title":"Identification of pests hidden in wheat kernels based on support vector machine classifier","volume":"17","author":"Zhihui","year":"2018","journal-title":"Instrumentation, Mesure, Metrologie."},{"issue":"107","key":"10.3233\/JCM-226642_ref4","first-page":"4521","article-title":"Research on time series modeling in grain storage hidden insects environment detection","volume":"28","author":"Li","year":"2019","journal-title":"Ekoloji."},{"key":"10.3233\/JCM-226642_ref6","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1016\/j.energy.2018.07.004","article-title":"Daily soil temperatures predictions for various climates in United States using data-driven model","volume":"160","author":"Xing","year":"2018","journal-title":"Energy."},{"key":"10.3233\/JCM-226642_ref8","doi-asserted-by":"crossref","unstructured":"Li X, Zhang X, Wang Y, Kaifeng Z, Chen YF. 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