Nonlinear Correction of Methane Sensor Based on Least Square Support Vector Machine
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摘要: 依据瓦斯传感器样本,文章提出了一种采用最小二乘支持向量机辨识传感器逆模特征的校正瓦斯传感器非线性误差的方法,详细介绍了SVM回归估计校正方法和LS-SVM校正方法的原理。该方法不需逆模型函数形式的先验知识,能够保证找到的极值解就是局最优解,具有较好的泛化能力。实例应用表明,采用该方法校正后的传感器的检测精度可达到0.4%,效果令人满意。Abstract: According to sample of methane sensor,the paper put forward a method of correcting nonlinear error of methane sensor,which can identify cont rary model characteristic of methane sensorcorrectly based on the least square support vector machine.It introduced principles of correction method of SVM regression estimation and LS-SVM in details.The method does not make use of any priori knowledge about contrary model function and can ensure that extremal solution is optimal and has generalization ability.The application result showed that the detection precision of methane sensor is 0.4% with the method,the result of nonlinear correction are great satisfaction.
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Key words:
- coal mine /
- methane sensor /
- nonlinear correct /
- least square support vector machine
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