Abstract: The study of the forecast on rockburst mostly remains at simple statistical study and the forecast of single factor, so the result is not satisfying. This thesis realizes rockburst mathematical model by adopting multilayer forward network and optimization BP algorithm based on genetic simulated annealing algorithm which has been used in the network training perceptrons. This optimization BP algorithm introduces simulated annealing mechanism to genetic algorithm and have it combined with multilayer perceptrons to form a compounding optimization perceptrons. The new perceptrons have both the learning ability of the neural network and robustness, and strong random searching ability of genetic algorithm. At the same time, this thesis makes a contrast study on the parameters of genetic simulated annealing algorithm by the principal performance index of genetic algorithm by using the practical monitoring data of Huafeng Mine rockburst and obtains a set of optimized parameters. By using those parameters, the structure weights and threshold value of the rockburst neural network model is optimized and obtains non-all-connected optimized neural network model. At last, this thesis carries out the short-term maximum magnitude of earthquake on Huafeng Mine rockburst by using this model. The relative error of the forecast is 7.84 % averagely. The forecast effect is comparatively ideal.

Key words: rockburst, multilayer perceptrons, genetic simulated annealing algorithm, forecast of earthquake intensity