针对人工神经网络技术在水文研究中出现的过拟合现象，从训练样本的构成入手，提出了两种新的训练策略，即择优检验和加权检验。前者通过计算比较，选择最优的训练样本；后者对不同训练样本的评定结果进行加权，以得到最终的结果。应用实例表明，此两种方法能有效地减轻神经网络中过拟合的缺点。

Aiming at the problem of over training in hydrologic research using artificial neural networks (ANNs) and considering the composition of training samples,two new learning strategies were presented,i.e,optimized detecting method and weighted detecting method.The former chooses the optimal training data after comparing the validation sets.The latter endues the different sets with different weights in order to get a final validation outcome.The application of the two new methods at Zipingpu Hydrographic Station on Minjiang River upstream indicates that they can resolve the problem of over-training effectively.