铁矿石烧结性能预报模型  

Predictive model of iron ore sintering capabilities

作　　者：范晓慧[1] 曾垂喜[1] 姜涛[1] 陈许玲[1] 龙红明[1] 胡友明 张克诚 

机构地区：[1]中南大学资源加工与生物工程学院,湖南长沙410083 [2]涟源钢铁公司,湖南娄底417009

出　　处：《中南大学学报（自然科学版）》2005年第6期949-954,共6页Journal of Central South University:Science and Technology

基　　金：国家自然科学基金资助项目(50374080)

摘　　要：研究了铁矿石烧结性能的评价指标及其主要影响因素,提出了误差修正的带动量项的线性再励自适应变步长BP神经网络算法,建立了铁矿石烧结性能预报模型。模型预报结果表明,用拓扑结构为12—34—4的BP神经网络训练6700次后,神经网络训练误差为0.000187,模型预报命中率均达83.5%以上,模型具有很好的泛化能力和自适应能力。The valuing indexes and some main influencing factors in iron ore sintering capabilities were investigated in this paper. Based on the research, a BP neural network learning algorithm with amending error, appending momentum and adaptive variable step size linear reinforcement was presented, and a predictive model of iron ore sintering capabilities was established. By adopting the BP neural network with the 12-34-4 structure and after 6 700 times train, the predictive result of model of iron ore sintering capabilities is satisfying, the neural network training error is 0.000 187, and the predictive hit-ratio of random samples is over 83.5%. It can be concluded that the predictive model is generally applicable and has self-adaptability.

关 键 词：铁矿石 烧结性能 神经网络 建模 预报 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]