Bowen Wang
China Coal Technology Engineering Group, Chongqing Research Institute, Chongqing, 410000, China.
Heliyon. 2023 Feb 28;9(3):e14055. doi: 10.1016/j.heliyon.2023.e14055. eCollection 2023 Mar.
After using a catalytic-combustion-based combustible-gas sensor (catalytic sensor) underground for a period of time, the sensitivity drifts due to environmental factors such as coal dust, temperature, and humidity. It is necessary to adjust the sensor regularly to ensure its accuracy. In this paper, RBF neural network technology is introduced to fit a nonlinear continuous function to solve the problem of the output error of the sensor being too large due to linear adjustment. Through experimental analysis, it is demonstrated that the RBF neural network model has a higher convergence speed and smaller error than other network models. By embedding the RBF network model into a sensor microcontroller, the error of traditional linear calibration can be reduced by two orders of magnitude and the measurement accuracy of the catalytic sensor can be greatly improved.
基于催化燃烧的可燃气体传感器（催化传感器）在井下使用一段时间后，由于煤尘、温度和湿度等环境因素，其灵敏度会发生漂移。有必要定期对传感器进行调校，以确保其准确性。本文引入径向基函数（RBF）神经网络技术来拟合非线性连续函数，解决因线性调校导致传感器输出误差过大的问题。通过实验分析表明，RBF神经网络模型比其他网络模型具有更高的收敛速度和更小的误差。将RBF网络模型嵌入传感器微控制器中，可将传统线性校准的误差降低两个数量级，大大提高催化传感器的测量精度。
