摘要
针对BP神经网络训练时间长的问题,采用基于案例推理的方法预测精炼开始钢水温度.首先,应用层次分析法确定影响精炼开始钢水温度的各个因素的权值,并使用灰色关联度来计算案例的相似度,克服了传统相似度计算方法在案例信息不完整的情况下无法获取准确结果的缺点.然后,提出一个包含类选、粗选、精选和择优的四步检索方法,大大缩短了检索时间.最后,实验比较了人工神经网络和基于案例推理两种方法,结果表明基于案例推理比人工神经网络具有更高的命中率.
Case-based reasoning was used to predict the starting temperature of molten steel in second refining so as to avoid the long training time of a BP （ back propagation） neural network. Analytic hierarchy process （AHP） was applied to determine the weights of factors influencing the starting temperature. Grey relational degree was adopted to compute the similarity between cases. Thus the shortcoming of difficulty in obtaining accurate cases with incomplete information is conquered. A four-step search method, including class search, rough search, delicate search, and optimized search, was provided, by which the search time decreases greatly. Experi- mental results using both artificial neural networks and case-based reasoning were compared. It is shown that case-based reasoning has got a higher hit rate and a shorter response time than artificial neural networks.
出处
《北京科技大学学报》
EI
CAS
CSCD
北大核心
2012年第3期264-269,共6页
Journal of University of Science and Technology Beijing
基金
"十一五"国家科技支撑计划重大项目"新一代可循环钢铁流程工艺技术"(2006BAE03A07)
中央高校基本科研业务费专项(FRF-AS-09-006B)
关键词
炼钢
精炼
温度
预测
基于案例推理
steelmaking
refining
temperature
prediction
case-based reasoning
