Based on the analysis of AE prediction parameters, an acoustic emission technology is currently one of the most important rock-burst prediction methods, in which the acoustic emission event rate, energy rate, and m values are used as the main predicting parameters. To build adaptive network fuzzy membership function, parameters must be normalized first. In the laboratory, the U.S. PAC company's DISP-24 acoustic emission test system is applied to obtain the data and the adaptive fuzzy neural network is applied to obtain membership functions. Experimental results show that the expected results of the performance can be achieved.
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