A Particle Swarm Optimization Based Grey Forecast Model of Underground Pressure for Working Surface

Forecasting of underground pressure for working surface (UPWS) plays an important role in mining technology industry for safety production. The characteristics of UPWS include roof lithologic, mining height of coal seam, geological structure, mining depth, promoting speed of working surface, influence of mining, etc. These factors directly affect the difficulty of forecasting trends in this field. This paper presents a new parameter optimization scheme of grey model (GM) using the particle swarm optimization (PSO) algorithm. The production coefficient of the background value is considered as decision variables and the forecasting mean absolute percentage error is taken as the optimization objective. Parameter optimization of GM is formulated as the combinatorial optimization problem and would be solved collectively using PSO technique. The model can be optimized once the PSO finds the optimal parameters of GM. GM with this parameter optimization algorithm is then applied in UPWS forecasting. Results of short and long terms forecasting show that PSO is an effective global optimization algorithm suitable for the parameter optimization of GM. The authors discuss implications of these findings for theory and practice.

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