【摘要】 为了优化BP神经网络，提出了一种优化BP神经网络的流程。首先，判断各影响因素之间的自相关性，如果各影响因素满足自相关评价指标，则可以使用BP神经网络进行回归训练；其次，改变BP神经网络的隐藏节点数、学习效率、训练误差和训练次数等影响因素；最后，加入遗传算法或者粒子群算法与BP神经网络组成混合算法，以提高BP神经网络的训练精度。更多还原

【Abstract】 In order to optimize the BP neural network, this paper proposes a process to optimize the BP neural network.First of all, judge the auto-correlation between each influencing factor. If each influencing factor meets the auto-correlation evaluation index, the BP neural network can be used for regression training. Secondly, change the number of hidden nodes, learning efficiency, training error, training times and other influencing factors of BP neural network.Add a genetic algorithm or particle swarm algorithm and BP neural network to form a hybrid algorithm to improve the training accuracy of BP neural network.更多还原