摘要
针对粮情测控系统中的粮食状态难以在线直接测量的问题,研究了工业测量和控制过程中的软测量技术.通过分析水分传感器、温度传感器及湿度传感器所测量的二次变量的结果,利用神经网络和模糊推理技术来实现智能化粮情测控过程中粮食的状态及其变化趋势的估计.
In this paper, the soft-measuring technique is applied to the prediction and monitor of grain states in a granary where it is very difficult that the grain states are monitored directly on-line. First the moistures, temperatures and humidity factors of a granary are measured by the sensors as the secondary variable. Then the estimations of the gain states and the tendency of the change are realized by a neural network and a fuzzy inference system with the results of secondary variable.
基金
河南省自然科学基金项目(0311011300)
关键词
模糊推理
软测量
储粮状态
粮情测控系统
水分传感器
soft-measuring
neural network
fuzzy inference system
sensor
secondary variable
primary variable
