摘要
由于温室环境受到各种因素影响,导致分布在各点的温度值不均匀,为了获得温度的准确值,提出了基于改进PSO的神经网络对其进行数据融合,并且采用分布图法剔除多传感器离异数据,最终得到准确有效的数据,为温室管理提供了精确的信息。仿真结果表明,采用这种方法可以提高温度采集的准确性,并且有效地消除了由于传感器失效引起的误差。
The temperature distribution in the greenhouse influenced by many kinds of environmental factors is uneven.In order to get precise data,the neural network based on improved PSO is proposed for greenhouse data fusion,and the distributing diagram approach is used to eliminate the careless mistake data.Data fusion technology gets efficient data,providing precise information for greenhouse＇s management.The results show that the precision of the collected data is improved and the careless error caused by disabled sensors is eliminated effectively.
出处
《计算机工程与应用》
CSCD
北大核心
2009年第17期218-220,共3页
Computer Engineering and Applications
基金
国家高技术研究发展计划(863)(No.2006AA10Z335)~~
关键词
温室温度
粒子群优化算法
传感器
数据融合
greenhouse temperature
Particle Swarm Optimization（PSO）
sensor
data fusion
