摘要：In order to accurately identify the change of shearer cutting load, a novel approach was proposed through integration of improved particle swarm optimization and wavelet neural network. An improved updating strategy for inertia weight was presented to avoid falling into the local optimum. Moreover, immune mechanism was applied in the proposed approach to enhance the population diversity and improve the quality of solution, and the flowchart of the proposed approach was designed. Furthermore, a simulation example was carried out and comparison results indicated that the proposed approach was feasible, efficient, and outperforming others. Finally, an industrial application example of coal mining face was demonstrated to specify the effect of the proposed system.
