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针对煤矿企业生产过程中安全生产状况的模糊性、多因素性等特点,利用Levenberg-Mrquardt优化算法改进BP神经网络并对其进行训练和仿真。并与标准BP算法和动量BP算法进行比较,发现经过改进的网络比标准BP算法和动量BP算法具有更好的效果。
Aiming at the fuzziness and multi-factors of production safety status of coal mines, Levenberg-Mrquardt optimization algorithm is used to improve BP neural network and to train and simulate it. Compared with the standard BP algorithm and the momentum BP algorithm, it is found that the improved network has better effect than the standard BP algorithm and the momentum BP algorithm.