%0 Journal Article
%A CUI  Dong-Wen-
%T Application of Discrete Hopfield Neural Network to the Assessment of Nutritional Status in Lakes and Reservoirs: A Case Study on 24 Lakes and Reservoirs in China
%D 2012
%R 10.3969/j.issn.1001-5485.2012.07.003
%J Journal of Yangtze River Scientific Research Institute
%P 10-14
%V 29
%N 7
%X Based on the associative memory of discrete Hopfield neural network, a model to  comprehensively assess the eutrophication level of lakes and reservoirs is established. Twenty-four lakes and reservoirs in China are evaluated through this model, and the results are compared with those of  projection pursuit method,  score index method, and LM-BP network method. The results show that discrete Hopfield neural network is simple, intuitive, and easy to implement, with only a few  iterations leading to satisfactory and objective results. However, not all eutrophication level assessments could be achieved through general discrete Hopfield neural network. When there is a big difference between each single index (factor), correct assessment could not be achieved.
%U http://ckyyb.crsri.cn/EN/10.3969/j.issn.1001-5485.2012.07.003