渔业水域污染死鱼的ANN识别模型的建立

本文应用人工神经网络（ANN）方法，对渔业水域污染死鱼特征进行提取，建立了导致污染死鱼的毒物类型的识别模型，其人工神经网络结构选输入层18个神经元，中间层9个神经元，输出层为1个神经元，并对网络参数的优化进行讨论。结果显示通过运用已训练好的渔业水域污染互鱼的B-P人工神经网络识别模型，只要通过简单的加法和乘法运算，就可以对任何一例污染死鱼的实际样本进行死鱼原因的判断，该方法具有较好的适用性和较高的实用性。

Indentification of the respective causes of toxic deaths in piscatorial waters with model ANN

Based on artificial neural network (ANN) and extracted characters of dead fish in polluted piscatorial waters, an identifiable model of toxicant type bringing on dead fish is presented in the paper. There are eighteen nerve cells in input-layer, nine nerve cells in middle-layer and one nerve cell in output-layer of the artificial neural network. Network parameters were optimized. Results show that any pollution to deaths can judge death causes by simple addition and multiplication operations using trained B - P artificial neural network, so this method has high applicability and practicability.