摘要
在混凝土智能材料和纤维材料的称量中,混凝土搅拌站的计量准确度是至关重要的。神经网络处理对无法建立确定模型的非线性输入输出映射关系,尤其是输入输出映射关系不断动态变化的场合具有得天独厚的优势,故本文中利用其对混凝土搅拌站的称重系统输入输出映射关系进行逆映射,以得到趋向称重系统输入真值的计量值,提高混凝土搅拌站的计量准确度。
The system measuring accuracy and quality of the mixed concrete of concrete central mixing station are essential for adding of intelligent material and fiber material. Neural networks, with their remarkable ability to derive meaning from complicated, imprecise nonlinear input output data, can be used to extract patterns and detect trends that are dynamically continuous change. Therefore, BP Neural Network is applied to map the input-output relation of the weighting system for concrete central mixing station so that the inputs can be accurately measured. The system measuring accuracy and quality of the mixed concrete of concrete central mixing station are increased.
出处
《传感技术学报》
CAS
CSCD
北大核心
2008年第6期1025-1028,共4页
Chinese Journal of Sensors and Actuators
关键词
混凝土搅拌站
高准确度称重
BP神经网络
concrete central mixing station
high accurate weighing
BP neural network
