摘要
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.
出处
《自动化学报》
EI
CSCD
北大核心
2004年第6期939-943,共5页
Acta Automatica Sinica
基金
国家"863"计划(863-51-945-011)国家自然科学基金(60274055)西安交通大学在职博士基金资助~~
关键词
神经网络
BP算法
网络结构
误差反传学习算法
高维小波网络
Computer simulation
Continuous casting
Hot rolling mills
Large scale systems
Learning algorithms
Mathematical models
Neural networks
Product design
Wavelet transforms