摘要
带钢热连轧生产过程中,轧制力预设定时的轧制力信号影响因素多、关联复杂,难以建立精确的机理模型.为此,文中应用小波多分辨分析方法,将轧制力分解重构为对应于不同影响因素的子信号,并建立了一个多RBF神经网络模型.模型中每个子网络分别对一个子信号进行建模,最后将各子网络输出综合为轧制力设定信号.各个子信号的影响因素不同,每个子模型输入参数和输出参数亦不同,从而能真实地反映轧制力变化的内在机理,具有明确的物理意义.仿真实验表明,这种建模方法降低了系统维数,能有效提高网络学习能力,轧制力预设定误差率从BP神经网络的10%降低到了5%.
During the setting of rolling factors with complicated correlation. It force in continuous hot strip rolling, force signals are influenced by is, therefore, difficult to establish an accurate model to describe the various rolling mechanism. In order to solve this problem, a multi-RBF neural network model is proposed. In this new model, the multi-resolution wavelet analysis method is employed to separate the rolling force signal into several sub-signals corresponding to different factors, and several RBF neural networks are established, each for a certain sub-signal. All the outputs of the sub-networks are integrated into a rolling force signal, and both the input and the output of each network relate to the affecting factors of the corresponding sub-signal. Thus, the sub-networks can well reflect the variation mechanism of the rolling force. Simulated results show that the proposed model decreases the number of system dimensions, improves the learning ability of the network, and reduces the error rate of rolling force setting from the original 10% in BP neural network model to 5%.
出处
《华南理工大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2010年第2期142-148,共7页
Journal of South China University of Technology(Natural Science Edition)
基金
国家自然科学基金资助项目(60774032)
广州市科技攻关重点项目(2007Z2-D0121)
关键词
热连轧
轧制力
小波分析
多RBF神经网络
continuous hot rolling
rolling force
wavelet analysis
multi-RBF neural network
