摘要
目的为解决微量包装系统中称量传感器输出电压与质量之间的非线性关系、提高称量精度,基于改进BP神经网络设计一种非线性补偿方法。方法阐述电阻应变式称量传感器的非线性补偿原理,根据称量传感器输入和输出之间的关系,设计一种神经网络补偿器。为提高神经网络控制性能,引入一种惩罚因子,可解决因训练不足导致的误差偏大等问题。结果经对比发现,改进型BP神经网络具有较快的收敛速度、较高的精度,可提高微量称量包装系统的控制性能。高速模式下,称量误差可以控制在0.5%以内,实际称量结果较理想。结论该方法能够改善系统动态性能,提高测量精度,可满足称量、包装行业等精度要求。
In order to solve the nonlinear relationship between the output voltage and quality of the weighing sensor in the micro-package system and improve the weighing accuracy, a nonlinear compensation method is designed based on the improved BP neural network. The nonlinear compensation principle of resistance strain type weighing sensor is described. According to the relationship between input and output of weighing sensor, a neural network compensator is designed. In order to improve the neural network control performance, a penalty factor is introduced to solve the problem of excessive error caused by insufficient training. By comparison, it is found that the improved BP neural network has faster convergence speed and higher precision, and can improve the control performance of the micro-weighing packaging system.In high-speed mode, the weighing error can be controlled within 0.5%, and the actual weighing result is relatively ideal. This method can improve the dynamic performance of the system, improve the measurement accuracy, and meet the requirements of weighing and packaging industry.
作者
冯云菊
FENG Yun-ju(Changzhou Institute of Industry Technology,Changzhou 213161,China)
出处
《包装工程》
CAS
北大核心
2021年第19期272-276,共5页
Packaging Engineering
基金
江苏省高等学校自然科学研究项目(19KJD470001)。
关键词
包装
称量
神经网络
非线性
package
weighing
neural network
nonlinear
