基于BP神经网络的电动堆高车货叉偏载检测

摘要

为了避免电动堆高车货叉在装卸货物时发生偏载安全事故,通过在试验车上安装偏载传感器来采集相关数据,利用神经网络工具箱训练数据来获得网络权值以及阈值并生成Simulink模型,再利用权值和阈值和Simulink模型建模的方法,构建基于BP神经网络的电动堆高车货叉偏载检测人工算法.对误差的分析验证表明:该算法可进一步提高检测货叉偏载距离的精度;该偏载检测算法应用到工程实践,能够满足电动堆高车货叉偏载检测的设计要求.%For avoiding the partial load security accident in the process of loading and unloading cargo with electric stacker truck forks,related data are collected through partial load sensors installed on the tested vehicles,and the weights and thresholds of network as well as the Simulink model were acquired from the training data of neural network toolbox.The artificial algorithm of partial load detection based on BP neural network was established by using of weights and thresholds and Simulink modeling.Calculation error analysis and verification demonstrate that the algorithm further improves the detection precision of partial load distance of forks,and engineering practice shows that this algorithm meets the design requirements of partial load detection.