摘要
利用生物发酵过程运行数据为样本，对BP神经网络进行训练，考察了网络结构（隐含层数、隐含结点数议及学习率η和动量参数α对收敛精度与收敛速度的影响，提出了稳定区的概念和多输出网络分割的思想，并给出了得到较好收敛速度与精度的学习方案。
Artificial Neural Network (ANN) was trained using biological fermentation data as samples.The effects of ANN structure(Hidden Layers Numbers & Units number in each Hidden Layer), learn rate ηand momentum constant α，on learn precision and speed were studied in details.We proposed the conception of stable area & multi-output network division, and provided optimized methods of Back-propagation (BP) training.
出处
《计算机工程》
EI
CAS
CSCD
北大核心
1998年第5期27-29,共3页
Computer Engineering
关键词
BP神经网络
收敛性
神经网络
Artificial neural network
Back propagation algorithm
Stable area
Network division
