The differences of the discharge processes verify the artificial pollution test results.
放电过程的差异验证了人工污秽试验的结果。
A 2-stages artificial neural network is trained to recognize different discharge patterns and the recognition rates are satisfying.
采用分级的人工神经网络进行放电模式识别，取得较好效果。
According to the relationship between precipitation and discharge of groundwater, a model of linear artificial neural network is set and used for dynamic prediction of discharge of groundwater.
根据降水量与地下水流量之间的相关关系，建立线性神经网络模型，并且将其用于地下水流量的动态预测。
Based on the time series, a model of linear artificial neural network is set and used for dynamic prediction of discharge of groundwater.
根据其时间序列，建立线性神经网络模型，并将其用于地下水流量的动态预测。
The feature of discharge is extracted using the 3D pattern chart and the artificial neural networks is used to recognize the discharge models.
采用三维谱图提取放电指纹特征，并用人工神经网络来识别不同的放电类型。
Coastal geologic environment of China is characterized by strong tectonic activities, huge silt discharge into sea, frequent surges, and prominent artificial environmental features.
我国海岸带地质环境，具有构造活动性强、入海泥沙大、多风暴潮、人工环境非常突出等特征。
Coastal geologic environment of China is characterized by strong tectonic activities, huge silt discharge into sea, frequent surges, and prominent artificial environmental features.
我国海岸带地质环境，具有构造活动性强、入海泥沙大、多风暴潮、人工环境非常突出等特征。
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