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中文摘要：
为了提高瓦斯传感器的精度和灵敏度,提出将改进的径向基函数神经网络（RBFNN）算法应用于瓦斯传感器系统中,对瓦斯传感器的非线性进行校正,同时分析了温度对瓦斯浓度预测的影响,然后利用RBFNN进行离散训练。实验结果表明,经过改进径向基函数神经网络（RBFNN）后,得到的数据比实际测量的瓦斯浓度要更接近于真实值,所产生的平均误差≤±0.1%。预测效果很好,达到了预期的技术指标,提高了瓦斯检测的灵敏度和精度。
英文摘要：
To improve the accuracy and sensitivity of methane sensor,the improved radical basic function neural network（RBFNN） algorithm is applied to the methane sensor system to correct the nonlinearity.The effect of temperature on the prediction of methane concentration is analyzed,and then RBFNN is used to do the discrete training.The experimental results show that the obtained data is closer to the true values than the actual measured concentrations through RBFNN,which causes that the average error is less than or equal to ±0.1%.The effect on the prediction is so good that it achieves the desired specifications,which improved the accuracy and sensitivity of methane detection greatly.
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