基于模糊神经网络的煤矿综采面安全风险辨识方法

Safety risk identification method of fully mechanized mining surface based on fuzzy neural network

摘要: 由于煤矿综采面作业环境复杂多变，导致传统的安全风险辨识方法难以全面、准确地反映实际情况，提出基于模糊神经网络的煤矿综采面安全风险辨识方法。采集并预处理煤矿综采面安全风险因素数据，构建一个模糊神经网络模型，输入预处理后数据，输出煤矿综采面安全风险等级辨识结果。实验结果表明，设计方法下煤矿综采面安全风险辨识结果的正确率高达97.22%，辨识精度较高。

Abstract: Due to the complex and changeable working environment of fully mechanized mining surface, the traditional safety risk identification method is difficult to reflect the actual situation, and the safety risk identification method of fully mechanized mining surface based on fuzzy neural network is proposed. Collect and preprocess the safety risk factor data of fully mechanized coal mine, construct a fuzzy neural network model, input the preprocessed data, and output the identification results of the safety risk level of fully mechanized coal mine. The experimental results show that the correct rate of the result is 97.22% and the identification accuracy is high.