摘要
水射流粉碎是一项新近发展起来的超细粉碎技术。由于水射流粉碎过程的复杂性，水射流粉碎机的参数设计建立在试验基础上．作者应用ＲＢＰ神经网络，在正交试验的基础上，建立了水射流粉碎过程的模型．研究结果表明，与ＢＰ网络相比．ＲＢＦ网络具有更高的精度和稳定性．所建立的模型可用于粉碎机参数优化．
Water jet grinding is a newly developed ultra-for comminution technology . Water jet mill has been designed largely by means of experiments because of its sophisticated crushing process. On the basis oforthogonal experiments , the prediction model of water jet mill grinding process is developed with artificial neural networds (ANN) in the paper. The simulation and experiment results show that the RBF (Radial BasisFunction) network is more accurate in predicting and stable in learning as well as recollecting. in comparison with BP network. The model eStablished has a good performance in sumulating the Crushing process andoptimizing the parameters of water jet mill.
出处
《有色金属》
CSCD
1999年第3期36-39,共4页
Nonferrous Metals
