Production Indices Prediction Model of Ore Dressing Process Based on PCA-GA-BP Neural Network
In order to determine the global production indices real-time completion situation after plans layer upon layers decomposition and transmition to working procedure and work team. A neural network model based on PCA-GA-BP was proposed to reasonable modify the production plan. The principle component analysis(PCA) was used to select the most relevant process features and to eliminate the correlations of the input variables; back-propagation(BP) neural network was used to characterize the nonlinearity and accuracy; genetic algorithm(GA) was employed to optimize the parameters and structure of the BP neural network by improving GA fitness function. Carried on prediction to weak magnetic concentrate taste and weak magnetic tailings taste according to actual production data. The Simulation results show that the proposed method provides promising prediction reliability and accuracy.
Weak Magnetic Process Production Indices Principle Component Analysis (PCA) Genetic Algorithm(GA) Back-Propagation(BP) Neural Network
Yefeng Liu Gang Yu Binglin Zheng Tianyou Chai
Key Laboratory of Process Industry Automation, Ministry of Education, Northeastern University, Sheny Key Laboratory of Process Industry Automation, Ministry of Education, Northeastern University, Sheny
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
2567-2572
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）