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现代矿业 ›› 2024, Vol. 40 ›› Issue (03): 230-234.
为了实现对一体化监测设备的可靠性综合评价，基于 T-S模糊神经网络的方法，以时 效性、粗差占比、采样间隔、误报次数4个指标作为输入向量，利用T-S模糊系统的数据处理能力、神 经网络的非线性拟合能力及MATLAB软件编程，构建一体化监测设备的综合评价模型。将训练好的 T-S模糊神经网络模型与BP神经网络模型同时用于6组监测设备的预测，从数值模拟与应用的情况 可以看出，普适型设备略强于一体化监测设备，与实际情况相符。该方法在一体化监测设备的评价 方面具有可行性和实用性，可以作为该设备在实践应用过程中可靠性的依据。
In order to realize the comprehensive evaluation of the reliability of the integrated monitor⁃ ing equipment，the method based on T-S fuzzy neural network is adopted，and four indicators such as time⁃ liness，gross error ratio，sampling interval and false positive number are taken as input vectors. The data processing capability of T-S fuzzy system，the nonlinear fitting capability of neural network and MATLAB software programming are utilized. A comprehensive evaluation model of integrated monitoring equipment is constructed. The trained T-S fuzzy neural network model and BP neural network model are applied to the prediction of 6 groups of monitoring equipment at the same time. From the numerical simulation and applica⁃ tion，it can be seen that the universal device is slightly stronger than the integrated monitoring device， which is consistent with the actual situation. The method is feasible and practical in the evaluation of the in⁃ tegrated monitoring equipment，and can be used as the basis for the reliability of the equipment in practice.
摘要： 为了实现对一体化监测设备的可靠性综合评价，基于 T-S模糊神经网络的方法，以时 效性、粗差占比、采样间隔、误报次数4个指标作为输入向量，利用T-S模糊系统的数据处理能力、神 经网络的非线性拟合能力及MATLAB软件编程，构建一体化监测设备的综合评价模型。将训练好的 T-S模糊神经网络模型与BP神经网络模型同时用于6组监测设备的预测，从数值模拟与应用的情况 可以看出，普适型设备略强于一体化监测设备，与实际情况相符。该方法在一体化监测设备的评价 方面具有可行性和实用性，可以作为该设备在实践应用过程中可靠性的依据。
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