Affiliations: [a] Key Laboratory of Grain Information Processing and Control (Henan University of Technology), Ministry of Education | [b] College of Information Science and Engineering, Henan University of Technology, Zhengzhou, Henan, China
Correspondence:
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Corresponding author: Yuhua Zhu, College of Information Science and Engineering, Henan University of Technology, Zhengzhou, Henan 450001, China. E-mail: youyou_70@126.com.
Abstract: When using the support vector regression method to predict grain storage temperature, it is challenging to choose the appropriate model parameters. Generally, it is effective to examine the trend of grain storage temperature in different layers after ventilation intervention. To enhance the performance of a support vector machine, it is necessary to choose an appropriate parameter optimization algorithm. 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.
Keywords: Grain temperature prediction, adaptive particle swarm optimization, support vector regression
