【摘要】 BP神经网络应用广泛，学习能力出众，但由于其训练速度较慢、容易受到极限的影响等原因，无法满足过于复杂的求解需求.相比之下，遗传算法可以有效地提高求解的复杂程度，并且可以通过对权重、阈值的调整来提升算法的准确性与可靠性.因此，文章提出了一种基于近地气候数据雷电预测模型，研究了网络参数对预测效果的影响.实验证明，该模型对比其他模型具有更好的预测能力和泛化能力.更多还原

【Abstract】 BP neural network has a wide range of applications and outstanding learning ability.However, it cannot meet the demand for solving problems that are too complex due to its slow training speed, susceptibility to local extremes and other factors.In contrast, genetic algorithms can effectively increase the complexity of problem solving and improve the accuracy and reliability of the algorithm by adjusting the weights and thresholds.Therefore, this paper proposes a lightning prediction model based on near-ground meteorological data.The experiment proves that this model has better prediction and generalization ability compared to other models.更多还原