Application of back propagation neural network optimized by different algorithms in prediction of Mooney viscosity of ethylene-propylene-diene monomer compound
Year: 2024
Type: article
Abstract: Genetic algorithm (GA) and particle swarm optimization (PSO) were used to optimize the back propagation（BP） neural network to establish the prediction model of Mooney viscosity of ethylene-propylene-diene monomer (EPDM) compound, and the error of the prediction results was compared and analyzed. The results showed that the predicted va-lues of the BP neural network model optimized by the two algorithms all maintained a high degree of fit and correlation with the measured values. Compared with the single BP neural network, the accuracy of the GA-BP neural network prediction model increased by 58.9% and the accuracy of the PSO-BP model increased by 3.57%, which indicated that the prediction accuracy of the prediction model optimized by the two algorithms, especially the BP neural network prediction model optimized by GA, improved significantly Mooney viscosity of EPDM compound.
Author: LI Gao-wei, LI Jia, ZHU Jin-mei, JIAN Ran-ran, MIAO Qing, ZENG Xian-kui
Institution: Qingdao University of Science and Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6926085314
FWCI:
Cites:
Cited by:
Related to: