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ren gong shen jing wang luo ji shu zai shui wen di zhi xue zhong de ying yong qian li chu tan
Author(s): 
Pages: 13-16
Year: Issue:  6
Journal: Hydrogeology and Engineering Geology

Keyword:  artificial neural network(ANN)self organization and self trainingnonlinear effects;
Abstract: 在许多水文地质问题中,多因素且非线性的影响常使传统的集中参数随机模型或分布参数确定性数值模型的方法难以对其作出符合实际的评价与预测。本文从几个典型的水文地质问题入手,利用人工神经网络技术的高度自组织、自适应与自学习能力和分类计算能力,对这些问题的解决进行了系统的BP网分析。结果表明,人工神经网络的应用可有效减少人为的主观臆断性,其训练识别的结果更符合实际,效果令人满意,因此具有十分广阔的应用前景。
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