摘要
在分析江河流域水情特点基础上,提出了流域多点水位信息融合聚类结构,通过ART-2神经网络和BP神经网络对影响流域水情的多点水位数据进行融合和输出空间聚类分析,研究了江河流域水情变化,而流域水位控制在期望值附近的聚类控制策略,实现江河流域水情的区域调度。
On the basis of analyzing the characters of the river basin flow situation, proposed several water level information merge and cluster structure of the river basin. Analyzing the water level information which influenced the river basin flow situation by means of merging and clustering output space through ART-2 nerve network and BP nerve network, the change of the river basin flow situation can be known and confirmed. The cluster control, which controls the river basin water level around the expected value, realizes the area dispatching of the river basin flow situation.
出处
《电子器件》
EI
CAS
2005年第2期304-306,361,共4页
Chinese Journal of Electron Devices
关键词
水位
神经网络
数据融合
聚类分析
控制策略
water level
nerve network
merging information
cluster analyzing
control strategy
