带钢热连轧生产过程中,影响因素多、关联复杂,轧制过程控制的精确模型难以建立,其中轧制力的预设定是重要问题之一,各种影响因素都会在轧制力的波动中有所体现.本文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不同频率成分子信号,并建立了一个多RBF网络模型,模型中每个子网络分别对一个信号成分进行建模,最后子网络输出被综合为轧制力设定信号.因为各个子信号影响因素不同,所以每个子模型输入参数不同，输出参数也不同,能真实地反映轧制力变化内在机理,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%.