摘要
鉴于水质类型和分级标准存在模糊性,将模糊数学中的相对隶属度理论和概率神经网络相结合,构建了模糊概率神经网络水质评价模型(FPNN)。阐明了该模型的构建方法,提出了基于指标相对隶属度矩阵插值构建训练样本的方法,并将该模型应用于实际水质评价。通过与综合评判法、属性识别法和BP网络法的比较,验证了该模型操作简便,评价结果客观可靠。
Considering the uncertainty of indexes for evaluating water quality and the standard of classification, Fuzzy Probabilistic Neural Network Model （FPNN） was proposed by combining the relative membership grade in fuzzy mathematics and Probabilistic Neural Network （PNN）. The process of this model was clarified, and the method of establishing the studied data based on relative membership grade matrix was brought forward. Finally the model was applied to the actual water quality evaluation. The result indicates that the proposed method is easy to operate and the outcomes is objective and credible compared with those by i.ntegrated evaluating method, attribute recognition model and BP network.
出处
《水文》
CSCD
北大核心
2007年第1期36-39,25,共5页
Journal of China Hydrology
基金
国家自然科学基金项目(N40101005)资助
关键词
模糊数学
相对隶属度
概率神经网络
水质评价
fuzzy mathematics
relative membership grade
probabilistic neural network
water quality evaluation
