Multi-input-layer Neural Network for Large-scale Industrial Product Quality Modeling
In this paper, a new architecture of wavelet neural network with multi-input-layer is proposed and implemented for modeling a class of largescale industrial processes. Because the processes are very complicated and the number of technological parameters, which determine the final product quality, is quite large, and these parameters do not make actions at the same time but work in different procedures, the conventional feed-forward neural networks cannot model this set of problems efficiently. The network presented in this paper has several input-layers according to the sequence of work procedure in large-scale industrial production processes. The performance of such networks is analyzed and the network is applied to model the steel plate quality of continuous casting furnace and hot rolling mill. Simulation results indicate that the developed methodology is competent and has well prospects to this set of problems.
Li Huan-qin Wan Bai-wu
Faculty of Science Xian Jiaotong University Xian, 710049 Systems Engineering Institute Xian Jiaotong University Xian, 710049
国际会议
青岛
英文
2006-07-21（万方平台首次上网日期，不代表论文的发表时间）