期刊文献+

霍尔传感器特性曲线的样条函数拟合方法 被引量:1

Characteristic Curve Fitting of the Hall Sensor Based on Cubic Splines Function
在线阅读 下载PDF
导出
摘要 为了对霍尔传感器特性曲线进行有效的拟合,分析了三次样条函数在霍尔传感器特性曲线拟合中的理论,应用软件Matlab 6.5结合实验数据对UGM3501U型传感器的UH-B曲线进行了拟合,给出了传感器拟合点减少的方案,这样可以减少成本和工作量,但对相对误差影响不大。结果表明,原拟合的曲线与减少拟合点后的拟合曲线两者都很光滑,且精度高,相对误差很小,这种方法有很强的实用性和较高的理论价值。 In order to increase effectiveness to fitting the characteristic curve of the Hall sensor, the theory of the cubic splines functions were analyzed. In Matlab 6.5, the experiments with synthetical data of the UH-B curve of UGN3501U type was done. And a scheme for reduce the fitting points was proposed, which can cut down the cost and the worklod and has little effect to the relative errortoo. The experimental resluts illuminate that the fitting curve which reducing the fitting points with our method is also soomth and has high precision and little relative error in comparison with the original curve. As a result, the method is more valuable in theory and has strong practicability at the same time.
出处 《微细加工技术》 2008年第2期8-11,56,共5页 Microfabrication Technology
关键词 霍尔传感器 样条函数 特性曲线 非线性校正 拟合 hall sensor splines function characteristic curve non-linear correction fitting
  • 相关文献

参考文献9

二级参考文献17

  • 1张广军,吕俊芳,周秀银,袁梅.红外线气体分析中环境温度和总压影响的补偿方法研究[J].计量学报,1996,17(3):174-177. 被引量:7
  • 2赵振宇 徐用.模糊理论和神经网络的基础与应用[M].北京:清华大学出版社,1992.85-87.
  • 3Perrand E. Theoretical model of performance of a silicon piezoresistive pressure sensor [ J]. Sensors and Actuators A, 1996,57 :245 - 252.
  • 4Elgamel H E A. A simple and efficient technique of capacitive pressure transducers [ J ]. Sensors and Actuators A, 1999,77:183 - 186.
  • 5林成森.数值计算方法[M].北京:科学出版社,1998..
  • 6Ngugen D. and B. Widraw. Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weight[C]. In:Procceeding of the International Joint Conference on Neural Networks 1990,3:21-26.
  • 7Poggio T.,Girosi F.Newworks for approximation and learning[J].IEEE Proc. 1990,4(9):1481-1497.
  • 8Park J, Sandberg I W. Universal approximation using radial basis function network[J].Neural Comutation,1991,3:246-257.
  • 9刘君华.智能传感器系统[M].西安:西安电子科技大学出版社,2000.264-289.
  • 10王旭东,邵惠鹤.RBF神经网络理论及其在控制中的应用[J].信息与控制,1997,26(4):272-284. 被引量:179

共引文献140

同被引文献2

引证文献1

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部