【摘要】 针对近年来河水污染日益严重的情况 ,论述了用反向传输神经网络 (BP网 )建立一种污染源监测模型 通过检测河水中每天超出正常阈值的化学污染物质和每个污染源排放废物中的化学污染物质 ,形成一组输入输出 ,来训练该神经网络 训练以后的神经网络的输出能模拟出每天河水中超出正常阈值的化学污染物质 从而找出造成河水污染的相关污染源 ,并分析出各污染源的污染程度更多还原

【Abstract】 This paper introduces an observation pattern of river pollution sources established by BP neural network to be directed against river pollution which is becoming more and more serious .First,a set of input and output is formed through monitoring the chemical pollutants beyond the normal threshold value and the chemical pollutants in the waste drained from every pollution source in the river every day.Then it is employed to train a neural network so that the output of the trained neural network can simulate the chemical pollutants beyond the threshold value in the river every day.In this way,it will be made possible to discover the pollution sources related to river pollution and analyze the extents by which they are polluting the river respectively.更多还原