计算机工程与应用2012，Vol.48Issue(11)：229-232,4.DOI:10.3778/j.issn.1002-8331.2012.11.049
EMD和遗传神经网络算法研究—以装载机动态称重系统为例
EMD method and genetic neural network algorithm and its application to dynamic weighing system for loader
摘要
Abstract
The output signal of pressure sensor installed in the dynamic weighing system for loader contains strong vibration, noise, nonlinear signal. The accuracy of the dynamic weighing system is closely related to the pressure signal. An Empirical Mode Decomposition (EMD) algorithm is proposed to preprocess the signal contaminated. The real weighing signal is filtered out. BP neural network is used to fit the nonlinear relationship between the weighing signal and the weights of the goods. The genetic algorithm is put forward to speed up the convergence. The suitable mathematical model of nonlinear measure weight is obtained. The emulation analysis and the results show that by using the above method, measure precision is efficacious.关键词
装载机/动态称重系统/经验模态分解/遗传神经网络Key words
loader/ dynamic weighing system/ Empirical Mode Decomposition (EMD)/ genetic neural network分类
信息技术与安全科学引用本文复制引用
刘勤贤,吕炜,鲍卫兵..EMD和遗传神经网络算法研究—以装载机动态称重系统为例[J].计算机工程与应用,2012,48(11):229-232,4.基金项目
浙江省自然科学基金(No.Y1100237). （No.Y1100237）
