摘要
卷取温度过程控制主要是通过传统数学模型进行描述，而层流冷却过程是一个非常复杂的非线性过程，尤其是对于低温卷取的温度控制，难以用数学模型精确表达．以攀钢热轧板厂层流冷却系统实测数据为基础建立采样数据的决策表，运用粗糙集理论将采样信息表进行模糊语言化，依据适合实际应用的语言数据关联规则支持度和可信度，通过属性约简，剔除冗余规则，挖掘出隐含的关联规则，通过动态的模糊模型的建立，优化传统层流冷却数学模型．实测数据运算表明，该方法可以将原模型的卷取温度控制精度提高1％～2％，具有很好的应用前景．
The process control of coiling temperature can be described mainly by mathematics, but laminar cooling is such a complex non-linear process that it can＇t be accurately described by mathematics especially at low temperature. According to the measured data of laminar cooling process during plate hot rolling in PanSteel, a decision table is given with sampled, in which the rough set theory is introduced to fuzz up linguistically the sample data so as to mine the implicit association rule by reducing attributes and rejecting redundant rules in accordance to the actual support/confidence level of association rule for linguistic data. Then, the conventional mathematic model for laminar cooling can be optimized by developing a fuzzily dynamic model. The actual operation with measured data shows that this method can improve the controlling precision of coiling temperature by 1% -2% and it has a great application potential.
出处
《东北大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2007年第11期1583-1585,1598,共4页
Journal of Northeastern University(Natural Science)
基金
国家自然科学基金资助项目(50104004)
关键词
粗糙集
关联规则
数据挖掘
层流冷却
属性约简
rough set
association rule
data mining
laminar cooling
attributes reducing
