中南大学学报（自然科学版）2012，Vol.43Issue(12)：4917-4922,6.
烧结配矿乏信息灰自助神经网络特征参数估计
Parameters estimation of sinter ore match poor information combined bootstrap method, grey analysis and artificial neural networks models
摘要
Abstract
An iron ore sinter evaluating function based on back propagation neural networks (BP) model was adopted to express and analyze complex sinter parameters information. The basic BP model consists of 13 input nodes, 27 hidden nodes and 2 output nodes. The input parameters included essential chemical composition parameters, quantity of conglutination particle and nucleolus particle of mixed ores; the output parameters consisted of sintering velocity and tumbler strength; and the hidden node was obtained through BP modeling with 100 groups of data for training and 50 groups of data for testing. Static information of experiments data was processed by Bootstrap method and change tendency information of process data was analyzed by grey analysis method. A kind of parameter estimation scheme was proposed based on BP model and statistics probability calculation. Sinter general and dynamic information was synthesized by the neural networks models, and estimation values of parameters within the ore evaluating functions were deduced by Monte Carlo simulation integrated statistics probability calculation. The estimation and simulation results provide an effective analysis means for sinter poor information.关键词
烧结配矿/乏信息分析/人工神经网络模型/Bootstrap方法/蒙特卡罗模拟Key words
sinter ore match/ poor information analysis/ artificial neural networks model/ Bootstrap method/ Monte Carlo simulation分类
矿业与冶金引用本文复制引用
刘代飞,李军,袁礼顺..烧结配矿乏信息灰自助神经网络特征参数估计[J].中南大学学报（自然科学版）,2012,43(12):4917-4922,6.基金项目
湖南省自然科学基金资助项目(11JJ4046) （11JJ4046）
