摘要
温室多参数控制系统是通过对影响温室环境的控制因子(如温度、湿度、光照和CO2浓度等)实现多参数控制。为此,利用基于密度的聚类算法(DBSCAN)和粒子群优化算法相结合的方式,将某一参数对温室影响的情况进行快速聚类,主要进行降维和归一化处理,以便找出适合温室的控制参数。实验证明,该算法对温室多参数控制具有很强的实用性。
Greenhouse multi-parameter control system is system which realize multi-parameter control such as temperature,humidity,light,CO2 concentration through the control of greenhouse environment factors,this paper mainly combine the use of density-based clustering algorithm(DBSCAN) and based on particle swarm optimization algorithm to realize the rapid clustering of the greenhouse impact of a parameter,the key task is to do dimensionality reduction and normalized treatment in order to gain the control parameters suitable for greenhouse experiments,the paper can show that the algorithm has a strong practicability for greenhouse parameters control.
出处
《农机化研究》
北大核心
2012年第10期180-183,共4页
Journal of Agricultural Mechanization Research
基金
黑龙江省教育厅面上项目(11511443)
关键词
温室
多参数
聚类算法
数据挖掘
greenhouse
multi-parameter
clustering algorithm
data mining
