Optimization algorithm of raw material ratio of zircon brick based on SVM and APSO

In order to determine the optimal raw material ratio of zircon brick more accurately and efficiently, an optimization algorithm of raw material ratio combining support vector machine (SVM) and adaptive particle swarm optimization (APSO) was proposed on the basis of traditional experiments. Firstly,...

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Bibliographic Details
Published in2021 International Conference on Information Technology and Biomedical Engineering (ICITBE) pp. 135 - 139
Main Authors Liang, Yufeng, Wei, Wenxue, Jiang, Jiajia, Qu, Yuanyuan
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2021
Subjects
Adaptation models
adaptive particle swarm optimization
Aggregates
Computational modeling
Prediction algorithms
Predictive models
Production
raw material ratio
support vector machine
Support vector machines
zircon brick
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Summary:In order to determine the optimal raw material ratio of zircon brick more accurately and efficiently, an optimization algorithm of raw material ratio combining support vector machine (SVM) and adaptive particle swarm optimization (APSO) was proposed on the basis of traditional experiments. Firstly, the SVM model of zircon brick bulk density and raw material ratio is established, and then the adaptive particle swarm optimization algorithm is used to optimize to find the optimal raw material ratio. Finally, the verification experiment is carried out. The results show that the combination of support vector machine and adaptive particle swarm optimization algorithm can effectively predict the raw material ratio to meet the production demand of zircon brick.
DOI:10.1109/ICITBE54178.2021.00038