基于关键输入和多输入层高维小波网络

High Dimension Wavelet Neural Network Based on Key Input and Multi-Input-Layer Structure

提出一种基于关键输入和加工工序的多输入层高维小波神经网络结构，该网络结构是在传统前馈神经网络的基础上，将一部分输入节点根据实际情况移到神经网络的相关隐层，关键输入节点不仅与随后一层隐节点相连，而且与输出节点相连，更真实地反映了大工业生产过程中变量之间复杂的函数关系．将该种小波网络模型应用于连铸连轧生产线产品质量建模，其效果较其他4种神经网络为优越．

This paper proposes a new architecture of high-dimension input wavelet neural network based on key inputs and work procedures for modeling complex large-scale industrial systems. In the neural network, some input variables are connected directly to the second hidden layer or other later hidden layers according to their actions being early or late in the work procedure. The key input nodes are connected to not only all nodes in the subsequent layer but also the output node. The developed method is applied to build a product quality model for continuous casting furnace and hot rolling mill. Simulation results demonstrate that the proposed network is more efficient and provides a higher accuracy compared with other four neural networks methods.