基于模糊聚类的PSO-神经网络预测热连轧粗轧宽度

为了提高热连轧粗轧宽度的控制精度,以攀钢热轧板厂实测数据为基础,采用粒子群优化算法训练神经网络并将其用于热连轧粗轧宽度预报,通过模糊聚类分析方法进行数据分析,科学选取学习样本,解决了由于样本多、学习速度慢的问题.实测数据运算表明,这种方法可避免神经网络陷入局部极小,带钢粗轧宽度的预报精度控制在6 mm以内,并且训练速度也有很大程度的改善,神经网络结构也得到优化,具有很大的应用潜力.

Hot Strip Width Prediction During Rough Rolling with PSO-Neural Network Based on Fuzzy Clustering

To improve the width control precision of hot strip during rough rolling for prediction, the PSO (particle swarm optimization) algorithm is used to train neural network with the actually measured data in PANSTEEL. Then, the fuzzy clustering analysis is used to preprocess the data with reasonable sampling for self-learning so as to solve the problem of superfluous samples and slow learning speed. The calculation results of measured data showed that this method can control the prediction precision of hot strip width during rough rolling within ±6 mm with optimized structure of neural network and learning speed greatly improved. So, it is regarded as highly potential in application.