摘要
Firstly, the early warning index system of coal mine safety production was given from four aspects as per- sonnel, environment, equipment and management. Then, improvement measures which are additional momentum method, adaptive learning rate, particle swarm optimization algorithm, variable weight method and asynchronous learning factor, are used to optimize BP neural network models. Further, the models are applied to a comparative study on coal mine safety warning instance. Results show that the identification precision of MPSO-BP network model is higher than GBP and PSO-BP model, and MPSO- BP model can not only effectively reduce the possibility of the network falling into a local minimum point, but also has fast convergence and high precision, which will provide the scientific basis for the forewarnin~ management of coal mine safetv production.
Firstly,the early warning index system of coal mine safety production was given from four aspects as personnel,environment,equipment and management.Then,improvement measures which are additional momentum method,adaptive learning rate,particle swarm optimization algorithm,variable weight method and asynchronous learning factor,are used to optimize BP neural network models.Further,the models are applied to a comparative study on coal mine safety warning instance.Results show that the identification precision of MPSO-BP network model is higher than GBP and PSO-BP model,and MPSOBP model can not only effectively reduce the possibility of the network falling into a local minimum point,but also has fast convergence and high precision,which will provide the scientific basis for the forewarning management of coal mine safety production.
