Title :
Based on Hopfield neural network to determine the air quality levels
Author :
Keyang, Li ; Runjing, Zhou ; Hongwei, Xu
Author_Institution :
Electron. Inf. Eng. Coll., Inner Mongolia Univ., Hohhot, China
Abstract :
Through puting the determination of the air pollution index as air quality level of the classification standard, this paper use the discrete Hopfield neural network to assort air for experiment. The Detail way is each attractor of system is a quality level of the air, and then treating the specific air samples as the initial input of the neural network. Association of the process is running toward a dynamic process of attractor. After the input state that pollution index sample convergenced to a certain attractor, its class is the class corresponding to the attractor of the system. Dividing the Hopfield neural network into two kinds, they are discrete and continuous Hopfield neural network. Here we use the discrete Hopfield neural network to classify quality levels of the air samples.
Keywords :
Hopfield neural nets; air pollution; environmental science computing; pattern classification; air pollution index; air quality level; classification standard; discrete Hopfield neural network; dynamic process; pollution index sample; Air pollution; Associative memory; Atmospheric measurements; Atmospheric modeling; Hopfield neural networks; Indexes; Neurons; air pollution index; air quality levels; attractor; discrete Hopfield neural network;
Conference_Titel :
Business Management and Electronic Information (BMEI), 2011 International Conference on
Conference_Location :
Guangzhou
Print_ISBN :
978-1-61284-108-3
DOI :
10.1109/ICBMEI.2011.5920947