摘要
针对象高炉这样复杂的 MISO 系统,本文提出了一种双层自校正预报方法,将快时变部分的递推辨识和慢时变部分的迭代修正相结合,较好地解决了各子模型时间常数大小和时变性快慢相差较大所带来的问题,并在高炉铁水含硅量预报的实际应用中获得了满意的结果.
A two-level self-tuning prediction method is proposed for a blast furnace,a complex MISO time-va-rying system.Combining the recursive identification of the faster time-varying part with the iterative cor-rection of the slower part,this method solves the problem of too big disparities in time constants andtime-varying speeds of submodels.Practice shows its effectiveness for predicting Si-content of molten ironin a blast furnace.
出处
《信息与控制》
CSCD
北大核心
1991年第1期19-22,共4页
Information and Control
基金
国家自然科学基金资助
关键词
自校正
预报
高炉
铁水
含硅量
system identification
adaptive prediction
two-level self-tuning prediction
blast furnace
