RESEARCH ON PERFORMANCE PREDICTION OF THIN SEAM SHEARER BY COMBINING GENETIC ALGORITHM WITH BP NEURAL NETWORK - ZHAO LiJuan; JIN ZhongFeng (Jixie qiangdu)
Increase the loading capacity of coal is an important part in the development of thin seam shearer. Optimization method based on the combination of genetic algorithm（ GA） and BP neural network was proposed for the problems that traditional method can`t solve about the multi Factor impact shearer coal capacity. Established mathematical model of thin seam shearer,we use genetic algorithm to optimize the weighted values and threshold values of the BP neural network,using the simulation data for training and testing samples,and then use the BP algorithm to train the neural network,thus avoiding the local minimum values when the training is done with the BP neural network alone. The result shows that method not only speeding up the convergence speed but also improve the training accuracy,also obviously valuable for the performance prediction of thin seam shearer.