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为了能够提高冶金矿山电爆网路早爆故障诊断的准确性,防止冶金矿山生产安全事故的发生,深入研究了模糊神经网络在其中的应用。分析了模糊神经网络的基本原理,冶金矿山电爆网路早爆故障的主要类型及原因。最后,分别利用模糊神经网络和传统人工神经网络对冶金矿山电爆网路早爆进行故障诊断,通过对诊断结果的比较可以发现,模糊神经网络具有较高的故障诊断精度和收敛速度,在冶金矿山电爆网路早爆故障诊断中有较广泛的应用前景。
In order to improve the accuracy of early blasting fault diagnosis of metallurgical mine explosion network and prevent the occurrence of production safety accident in metallurgical mine, the application of fuzzy neural network is deeply studied. The basic principle of fuzzy neural network is analyzed and the main types and causes of early-burst faults of electric blasting network in metallurgical mine are analyzed. Finally, the fuzzy neural network and the traditional artificial neural network are respectively used to diagnose the early explosion of the electric blasting network in the metallurgical mine. By comparing the diagnostic results, it can be found that the fuzzy neural network has high fault diagnosis accuracy and convergence speed. There is a wide range of application prospect in the early blasting fault diagnosis of mine electric burst network.