基于小波多分辨分析的新型多RBF网络轧制力设定模型

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％.

A Novel Multi-RBF Neural Network Model for Rolling Force Setting Based on Multi-Resolution Wavelet Analysis

Aiming at the problem of rolling force setting, a multi-RBF neural network model based on the multi-resolution wavelet analysis is proposed. In a hot strip rolling mill, the number of system parameters and disturbance factors is large, the relationships between each of them are complicated, so it is difficult to obtain accurate mathematical models for the process control, including the one for rolling force setting. The multi- resolution wavelet analysis method is employed to decompose the rolling force signal and reconstruct it as a serial of sub-components. Each sub-component has its own affecting factors. Then a multi-RBF neural network with several sub-networks is established, each sub-network is for the modelling of each sub-component, and their outputs values are added up as the rolling force value. Different to those sub-networks in other multi-model modelling methods, the sub-networks in the proposed method have different input parameters and outputs, so they can truly reflect the variation mechanism of the rolling force. Simulation results show that this proposed model can reduce the system dimension, and improve the learning ability of the network. The error rate of rolling force setting is reduced from 10% by BP neural network model to 5% by the proposed model.