Error Analysis of Coal-rock Identification Based on BP Neural Network Optimization Algorithm
The convergence speed of BP(Back Propagation)neural network is slow.In order to improve the application level of BP neural network,the PSO(Particle Swarm Optimization)algorithm is used to optimize BP neural network,and a PSO-BP neural network algorithm integrating the advantages of two algorithms was proposed.Based on the understanding of the application principle of BP neural network optimization algorithm,the feasibility of applying BP neural network optimization algorithm in coal-rock identification was studied in combination with a coal mine project example.The research object was the traditional BP neural network and the optimized PSO-BP neural network.The error boundary and other parameters were set,and the application effect of the two methods was evaluated according to the error curve and prediction results.The research shows that the characteristic value of PSO-BP neural network algorithm is suitable and the recognition accuracy is high;the results of coal-rock recognition based on BP neural network optimization algorithm have high consistency with the actual situation,and the evaluation results based on this optimization algorithm can provide guidance for coal mine production.
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