Rolling Force Prediction Based on Support Vectors Machine with Particle Swam Optimization
YANG Jing-ming 1,2, CHEN Wei-ming1,2,CHE Hai-jun1,2,LV Jin3,JIA Lin1,2
1. College of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
2. Key Lab of Industrial Computer Ctrl Eng of Hebei Province, Yanshan University, Qinhuangdao,Hebei 066004,China
3. Tianjin Design and Research Institute of Electric Drive, Tianjin 300180,China
Abstract：In order to improve the prediction accuracy of rolling force in hot rolled strip, combined with particle swarm optimization algorithm and support vector machine method to predict the rolling force, According to the principle of rolling, rolling force prediction model is established by using SVM, the parameters of SVM was optimized by particle swarm algorithm to improve the prediction accuracy. In order to further improve the precision of rolling force prediction, network based on SVM combined with mathematic model method is proposed. The acquisition of a large number of rolling data offline simulation was based on a “1 + 4” aluminum strip rolling factory. The simulation results can be seen that rolling force predicted the method of network based on SVM combined with mathematic mode, improves the speed of rolling force prediction and makes the forecast accuracy of rolling force control within 7%.
杨景明,陈伟明,车海军,吕金,贾林. 基于粒子群算法优化支持向量机的铝热连轧机轧制力预报[J]. 计量学报, 2016, 37(1): 71-74.
YANG Jing-ming,CHEN Wei-ming,CHE Hai-jun,LV Jin,JIA Lin. Rolling Force Prediction Based on Support Vectors Machine with Particle Swam Optimization. Acta Metrologica Sinica, 2016, 37(1): 71-74.
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2016, Vol. 37 
