摘要
分别采用遗传算法(GA)和粒子群算法(PSO)优化的back propagation(BP)神经网络建立了三元乙丙橡胶(EPDM)混炼胶门尼黏度的预测模型,并对预测结果的误差进行了对比分析。结果表明,两种算法优化后的BP神经网络模型的预测值与实测值均保持较高的拟合度和相关性;相比单一的BP神经网络,GA优化后BP神经网络模型的精度提高了58.9%,PSO优化后BP神经网络模型的精度提高了3.57%,说明两种算法优化后的预测模型,特别是GA优化的BP神经网络预测模型对EPDM混炼胶门尼黏度的预测精度改善明显。
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.
作者
李高伟
李佳
朱金梅
鉴冉冉
苗清
曾宪奎
LI Gao-wei;LI Jia;ZHU Jin-mei;JIAN Ran-ran;MIAO Qing;ZENG Xian-kui(School of Mechanical and Electrical Engineering,Qingdao University of Science and Technology,Qingdao 266061,China)
出处
《合成橡胶工业》
CAS
北大核心
2023年第6期488-494,共7页
China Synthetic Rubber Industry
基金
国家自然科学基金资助项目(52206095)
山东省自然科学基金资助项目(ZR 2021 QE 232)。
