A fuzzy neural network prediction model based on rough set for characteristic variables of blasting vibration
Chen Shou-ru · Zhendong yu chongji · 2009
Characteristic variables of blasting vibration have great efiects on its damage level.The prediction of characteristic variables caused by blasting vibration is helpful to study blasting vibration effect.Here,the prediction of the amplitude and the first dominant frequency band and its time duration were achieved on the basis of rough set and fuzzy neural network(FNN)theory.The purpose of this study was to explore a method which could avoid the limitation of the prediction with only one index and to improve the prediction precision.Firstly,the drawback of the prediction of the am-plitude based on Sadov's vibration formula was analyzed.Secondly,rough set and fuzzy neural network(RSFNN)theory were introduced briefly.Thirdly,a rough set-based FNN prediction model for characteristic variables of blasting vibration was established based on analysis of factors affecting blasting vibration characteristic variables.Finally,the model was trained with data come from Tonglvshan Copper Mine and was tested by 1 5 groups of data.The results showed that the rough set and FNN prediction model reflected the nonlinear relationship between factors and characteristic variables and could be used to predict characteristic variables of blasting vibration.It was also found that the precision of predicting sin-gle index a time was higher than that of predicting three indexes at the same time.