摘要
在实际生产过程中,传统轧制力数学模型存在较大误差,影响计算精度.提出将BP网络与修正遗传算法相结合,利用BP网络的指导性搜索思想和遗传算法的全局搜索能力预测中厚板轧机轧制力,并建立预测模型.同时,根据模型编制相应的程序及界面.以邯钢中板厂、普阳中板厂现场数据为基础,通过数据优选,选择较优数据进行离线轧制力预测,预测精度优于传统的数学模型,预报精度的相对误差可以控制在4%以内,能够满足生产需要.
In the process of real production, the calculation results by conventional models have relatively big errors in the prediction of rolling force. It is therefore suggested to combine the BP network with modified GA algorithm to develop a prediction model, i.e. , predicting the rolling force to be applied to medium thick plates by means of the guidable idea about search from BP network and the capability of global search from genetic algorithm. Moreover, the corresponding program and interface were given according to the prediction model. Based on the in-situ data taken from Handan and Puyang plate rolling mills, rome optimal data was selected to predict the off-line rolling force. The results showed that the predicted accuracy resulting from the model developed is higher than that from conventional models, i.e., the relative error is within 4 %, thus meeting the actual requirement for plate rolling.
出处
《东北大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2008年第10期1438-1442,共5页
Journal of Northeastern University(Natural Science)
基金
国家自然科学基金资助项目(50604006)
关键词
人工神经元网络
BP算法
遗传算法
轧制力预测
数学模型
ANN （artificial neural network）
BP algorithm
GA （genetic algorithm）
rolling force prediction
mathematic model
