| 基于RBFNN的铂电阻温度传感器非线性补偿 |
| |
| 引用本文： | 俞阿龙.基于RBFNN的铂电阻温度传感器非线性补偿[J].传感器与微系统,2005,24(12):43-45. |
| |
| 作者姓名： | 俞阿龙 |
| |
| 作者单位： | 淮阴师范学院,物理与电子学系,江苏,淮安,223001 |
| |
| 基金项目： | 江苏省高校自然科学基金 |
| |
| 摘 要： | 针对铂电阻温度传感器应用中存在的非线性问题,提出了应用径向基函数神经网络(RBFNN)强非线性逼近能力进行铂电阻温度传感器非线性补偿的方法。介绍了非线性补偿的原理和网络训练方法。结果表明:这种非线性补偿模型具有误差小、精度高、可在线标定和鲁棒性强等优点,与基于BP神经网络的非线性补偿模型相比,大大缩短了网络训练时间,从而方便了铂电阻温度传感器在测控系统中的应用。
|
| 关 键 词： | 铂电阻 径向基函数神经网络 非线性补偿 |
| 文章编号： | 1000-9787(2005)12-0043-03 |
| 收稿时间： | 06 21 2005 12:00AM |
| 修稿时间： | 2005年6月21日 |
Nonlinearity compensation of platinum resistor temperature sensor based on RBFNN |
| |
| YU A-long.Nonlinearity compensation of platinum resistor temperature sensor based on RBFNN[J].Transducer and Microsystem Technology,2005,24(12):43-45. |
| |
| Authors: | YU A-long |
| |
| Abstract: | A method to compensate nonlinearity of platinum resistor temperature sensor is presented using nonlinearity compensation model founded by RBFNN to aim at its nonlinear problem. The principle of nonlinearity compensation and training method of neural network are introduced. The results show that nonlinearity compensation model has character of small error, high precision, on-line scaling, strong robustness and_fast network training speed compared with the compensating method of BP neural network model. It makes convenient for platinum resistor to be applied in the field of measurement and control. |
| |
| Keywords: | platinum resistor radial basis funchon neural network(RBFNN) nonlinearity compensation |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
