RBF神经网络与模糊理论相结合的磨矿分级智能控制方法

Intelligent control of the grinding and classification system based on fuzzy RBF neural network

将RBF神经网络和模糊理论结合起来,提出了一种基于RBF神经网络和模糊理论实现智能控制的方法。该方法能够有效克服磨矿效率和旋流器入口压力等波动引起的扰动,使磨矿浓度和溢流粒度的波动减小,为浮选过程产品品位改善及产量提高创造了有利条件,在技术上实现了优化磨矿分级过程。该分析过程相对简单,网络学习训练时间少,学习精度高,估计值与分析值拟合非常好。仿真表明这类智能控制器可用于难以建立数学模型的控制系统。

Based on RBF neural network and fuzzy theory,an intelligent control method,which can effectively overcome disturbance resulting from grinding efficiency and cyclone＇s inlet pressure,is proposed.This method that can make grinding concentration and overflow particle size well-proportioned will allow us to improve flotation grade and increase yield,and therefore realize the optimization of grinding and classification process.The present method is of simple analysis,less time of network learning and training.And high learning precision is high.The simulations show that our approach can also be applied to the control systems that are difficult to build accurate math model.