中国机械工程 ›› 2012, Vol. 23 ›› Issue (4): 430-434.

• 信息技术 • 上一篇    下一篇

基于灰色BP神经网络的质心数据处理方法

张宪;钟江;吴晖;赵章风
  

  1. 浙江工业大学,杭州,310014
  • 出版日期:2012-02-25 发布日期:2012-03-02
  • 基金资助:
    国家“十二五”科技支撑计划资助项目(2011BAD29B12);国家自然科学基金资助项目(50805131) 
    The National Key Technology R&D Program(No. 2011BAD29B12);
    National Natural Science Foundation of China(No. 50805131)

Data Processing Method of Centroid Test System Based on Gray BP Neural Network Algorithm

Zhang Xian;Zhong Jiang;Wu Hui;Zhao Zhangfeng
  

  1. Zhejiang University of Technology,Hangzhou,310014
  • Online:2012-02-25 Published:2012-03-02
  • Supported by:
     
    The National Key Technology R&D Program(No. 2011BAD29B12);
    National Natural Science Foundation of China(No. 50805131)

摘要:

为减小传感器的随机误差对三点支撑小型农业机质心测试系统质心高度数据的影响,同时提高质心高度的测试效率和安全性,对该类测试系统在有限次、小角度条件下测试获得质心高度的数据处理方法进行了研究。提出灰色GM(1,1)模型与BP神经网络相结合的灰色BP神经网络模型的构建方法,
采用该方法构建的预测模型,对质心高度测试获得的数据进行处理可以获得较高精度的质心高度数据,并搭建了小型试验台对预测模型进行验证。结果表明,采用该方法构建的质心高度预测模型得到的质心高度数据相对误差为0.759%。

关键词:

Abstract:

In order to reduce the influences of sensor's random errors on three point support small agricultural machinery centroid position measuring system,a new test data processing method was studied with fewer times and small-angle test operations.A construction method of gray BP neural network model was proposed,which integrated the gray GM(1,1) model and the BP neural network model.And the prediction model was verified with an actual small agricultural machinery centroid measuring platform.The results show that the construction method and forecasting model are credible,and the relative error is as 0.759% which is better than original error.

Key words: data processing, forecasting model, gray GM(1,1) model, BP neural network

中图分类号: