@article{1844,
  author = {Mei Chen},
  title = {Algorithm on Evaluation of Atmospheric Inhalable Particles in Beijing based on Extension Neural Network},
  journal = {Journal of Digital Information Management},
  year = {2015},
  volume = {13},
  number = {5},
  doi = {},
  url = {http://dline.info/fpaper/jdim/v13i5/v13i5_7.pdf},
  abstract = {The research aimed to put forward a kind of
new network structure on the basis of extension theory,
namely extension neural networks (ENN), in order to
improve the accuracy of atmospheric pollution
assessment, and solve the contradictory interval division
problem, avoid falling into the defect of local optimum,
improve the speed of network training, and also shorten
the operation time. This study collected historical
monitoring data of 12 districts in Beijing as samples,
considered principal pollutants including SO2, NO2 and
inhalable particle PM10 as network input, and also set up
and tested network taking extension distance as measure
tool. The results showed that prediction results basically
met the requirements of accurate assessment compared
with the actual results, which confirmed the feasibility,
accuracy and effectiveness of the algorithm. In addition,
compared with structure and training speed, ENN network
had more obvious superiority than BP network.},
}