摘要
分析中厚钢板板凸度计算模型并给出相应的在线数学模型。为了提高板凸度在线模型预测精度,提出了一种基于模糊聚类BP神经网络的板凸度模型影响系数的优化方法。并采用模糊聚类分析方法,科学选取学习样本,解决了样本多、学习速度慢的问题。通过大量在线数据分析,可知这种方法对中厚板板凸度的预报精度有很大改善,能适应不断变化的工艺过程和设备条件。
This paper analyzes a simple plate crown model and gives an on-line model relevant to it.A new method is proposed to optimize plate crown model basis on BP neural networks to improve the control accuracy of crown predicted.The fuzzy cluster analysis was used as the preprocessing to select the sample set, which can increase the speed of study.It is shown that the prediction precision of BP neural networks can be improved greatly according to many on-line data and the optimization model can be adapted to varied techniques and equipment.
出处
《塑性工程学报》
EI
CAS
CSCD
北大核心
2005年第4期58-61,共4页
Journal of Plasticity Engineering
基金
国家重点基础研究发展规划基金项目(G20000672084)。
