摘 要：随着成千上万种化学物质的产生并排放到水中，化学物质的环境危险评价越来越重要。虽然部分有机毒性物的实验测定现在己能做到，但由于这些毒性物的数量非常众多并且存在其他一些潜在的有机物仍未进行实验测定，所以定量预测环境中有机毒性物的毒性仍是十分重要和必须的。本论文主要讨论径向基人工神经网络（Radial Basis Function Network，RBF）用于胺类有机物毒性识别问题，内容包括有机物的环境影响评价的意义，胺类有机物对环境的影响分析，RBF人工神经网络建模的思想、结构和算法，以及胺类有机物QSAR（定量结构-活性相关，Quantitative Structure-Activity Relationship）分类问题RBF实现的基本设定。实验部分是以胺类有机物中的各个变量为基础做出建模，并使用MATLAB软件进行相关程序的运行，进而做出数据处理和结果分析，计算其结果的准确性，并和多元线形回归方法进行比较。
关键词：环境影响评价；胺类有机物；毒性识别；人工神经网络；RBF
Abstract：With thousands of chemicals producing and releasing into the water, chemical evaluation of the environment is more important. However, some of the experimental determination of organic toxic now can be done, but because these were the toxicity of a very large number as well as the existence of other potential organic matters that have not been measured yet, therefore, the quantitative estimation of the toxicity of organic toxic material is still important and necessary. This report mainly discussed RBF artificial neural network (radial basis function network. RBF) for the identification of toxic organic amines, including environmental impact assessment of the significance of the organic matters, analysis of the environmental impact of organic amines, RBF artificial neural network modeling ideas, structure and algorithm, and organic amines QSAR (Quantitative Structure-Activity Relationship) RBF classification, the basic set. Experimental part is based on various variables of the organic amines and make modeling, use the software of MATLAB for the operating the related procedure, then make the data processing and result analysis, calculate the accuracy of the result, and compare with the way of multiple linear regression.
Key words：Environmental impact assessment; Organic amines; Toxicity identification; Artificial neural network; RBF;
