| Nonlinear inverse modeling of sensor based on back-propagation fuzzy logical system |
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| 作者姓名： | 李军 刘君华 |
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| 作者单位： | [1]School of Electrical Engineering, Xi＇an J iaotong University, Xi＇an 710049, China [2]School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China |
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| 摘 要： | Objective To correct the nonlinear error of sensor output, a new approach to sensor inverse inodeling based on Back-Propagation Fuzzy Logical System （BP FS） is presented. Methods The BP FS is a computationaiiy efficient nonlinear universal approximator, which is capable of implementing complex nonlinear mapping from its input pattern space to the output with fast convergence speed. Results The neuro-fuzzy hybrid system, i.e. BP FS, is then applied to construct nonlinear inverse model of pressure sensor. The experimental results show that the proposed inverse modeling method automatically compensates the associated nonlinear error in pressure estimation, and thus the performance of pressure sensor is significantly improved. Conclusion The proposed method can be widely used in r nonlinearity correction of various kinds of sensors to compensate the effects of nonlinearity and temperature on sensor output.
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| 关 键 词： | BP算法 模糊逻辑系统 传感器 非线性可逆模型 |
| 文章编号： | 1671-8267（2007）01-0014-04 |
Nonlinear inverse modeling of sensor based on back-propagation fuzzy logical system |
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| Li Jun,Liu Junhua.Nonlinear inverse modeling of sensor based on back-propagation fuzzy logical system[J].Academic Journal of Xi’an Jiaotong University,2007,19(1):14-17. |
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| Authors: | Li Jun Liu Junhua |
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| Abstract: | Objective To correct the nonlinear error of sensor output,a new approach to sensor inverse modeling based on Back-Propagation Fuzzy Logical System(BP FS) is presented.Methods The BP FS is a computationally efficient nonlinear universal approximator,which is capable of implementing complex nonlinear mapping from its input pattern space to the output with fast convergence speed.Results The neuro-fuzzy hybrid system,i.e.BP FS,is then applied to construct nonlinear inverse model of pressure sensor.The experimental results show that the proposed inverse modeling method automatically compensates the associated nonlinear error in pressure estimation,and thus the performance of pressure sensor is significantly improved.Conclusion The proposed method can be widely used in nonlinearity correction of various kinds of sensors to compensate the effects of nonlinearity and temperature on sensor output. |
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| Keywords: | sensor inverse modeling fuzzy logical system back-propagation algorithm |
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