A New Training Algorithm for the Process Neural Network and Its Application
This paper presents a new training aigodthm for the process neural network(PNN)when it is used to model an industrial process.On the base of the pretreatment for the process discrete data considering their including some pseudo ones，a new training algorithm based on discrete Walsh conversion Was used to convert the discrete data to be the direct inputs of PNN，which can shorten the PNN training time and improve the PNN mapping capability.The PNN model with the new training algorithm and two hidden-layers structure Was appfied tO forecast the mycefium density of the glutamate fermentation process，and the simulation results were excellent.
过程神经网络 沃尔什变换 预处理 菌体浓度 发酵过程
Guan Shouping Peng Jun
Key Laboratory of Process Industry Automation,Ministry of Education,Northeastern University,Shenyang,Liaoning Province China,110004
国内会议
南京
英文
83-88
2008-02-01（万方平台首次上网日期，不代表论文的发表时间）