优化RBF神经网络在压力传感器中的应用
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辽宁教育厅优秀人才计划基金资助项目(2008RC25)

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The Application of Optimized RBF Neural Network to Pressure Transducer
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    摘要:

    针对压力传感器在应用中出现温度误差大这一缺点,提出了通过采用径向基函数(RBF)神经网络较强的非线性逼近能力,实现其非线性校正和温度补偿的网络方法,并对该法进行改进。通过仿真可看出,改进方法校正的系统能自动补偿非线性误差,具有误差小,精度高等优点。因此,提出的改进的RBF神经网络法对压力传感器的非线性补偿是可行的。

    Abstract:

    Aiming at the drawback of large temperature error of the pressure sensor,a network method making use of the radial basis function (RBF) neural network which has the strong ability of non linear approximation was proposed to realize the non linear correction and temperature compensation,and the method was also improved in this work.The simulation results showed that the non linear errors of the system corrected by this improved method could be compensated automatically.The improved RBF neural network method has the features of small error,high precision and thus is feasible to compensate the non linear error the pressure sensor.

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彭继慎,程英.优化RBF神经网络在压力传感器中的应用[J].压电与声光,2012,34(3):414-416. PENG Jishen, CHENG Ying. The Application of Optimized RBF Neural Network to Pressure Transducer[J]. PIEZOELECTRICS AND ACOUSTOOPTICS

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  • 在线发布日期: 2012-09-27
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