Abstract: A rockburst mathematical model is established by three layers neural network; and a new algorithm which is called asynchronous iterative algorithm with relaxation factor is trained the model. The algorithm has a better speed and avoids local minima and paralysis in some degree. As a results, maximum magnitude of rockburst is forecasted. The paper adopts 1999—2000’s field rockburst data of Huafeng Coal Mine of Xinwen Mining Company and turns rockburst energy, wave amplitude and frequency into neural network input, turns related maximum magnitude of rockburst with above data into neural network output. So, we set up three layers forward neural network with three input nodes, seven hiden layers nodes and one output node. The paper selects 35 groups from 40 groups in 1999—2000 year and turns it into training groups, the remains is regarded as testing groups. The training program is designed by Matlab 6.1. After 50 000 iterations, the network error is 0.06 and the value is under the network’s error precision. So, the network is convergence. At last, average error between the network forecast value and testing value is 9.2﹪,the result verifies that the method is successsful.

Key words: rockburst, BP neural network, asynchronous iterative algorithm, relaxation factor