均方误差
反向传播
近似误差
人工神经网络
振动
粒子群优化
均方根
数学
振动器
统计
计算机科学
生物系统
人工智能
算法
声学
工程类
物理
生物
电气工程
作者
Jinxia Zhang,Yan Wang,Fujun Niu,Hongmei Zhang,Songyi Li,Yanpeng Wang
出处
期刊:Minerals
[MDPI AG]
日期:2023-07-11
卷期号:13 (7): 929-929
摘要
It was possible to conduct a study on the shape and parameterization of the vibrating screen so as to explore the relationship between detailed vibrating screen motion parameters and particle group distribution under different screen surface states. The motion characteristics of particle groups in various scenes were investigated, screening performance of vibrating screen with complex parameters was studied, interaction between motion parameters of screen surface and motion of material groups in multi-component mixed particle groups was analyzed, segregation distribution law of multi-component mixed material groups was revealed, and this study presents simulation findings based on the discrete element program EDEM. The ensemble learning approach was used to examine the optimized model screen. It was revealed that the screen’s amplitude, vibration frequency, vibration direction angle, swing frequency, swing angle, and change rate of screen surface inclination all had a major impact on its performance. As a result, the vibrating screen’s running state was described by various parameter combinations, and the trend changes of several factors that affected the performance of the screen were examined. The investigation revealed that the particle swarm optimization backpropagation (PSO-BP) neural network model outperformed the backpropagation (BP) neural network model alone in terms of prediction. It had lower root mean square error (RMSE), mean square relative error (MSRE), mean absolute error (MAE), and mean absolute relative error (MARE) than the BP neural network model, but a larger R2. This model’s greatest absolute error was 0.0772, and its maximum relative error was 0.0241. The regression coefficient R value of 0.9859, which displayed the model’s strong performance and high prediction accuracy, showed that the PSO-BP model was feasible and helpful for parameter optimization design of vibrating screens.
科研通智能强力驱动
Strongly Powered by AbleSci AI