Based on Hopfield neural network to determine the air quality levels
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.
airqualitylevels airpollutionindex discreteHopfieldneuralnetwork attractor
Li Keyang Zhou Runjing Xu Hongwei
Electronic Information Engineering CollegeInner Mongolia UniversityHohhot, China Hetao Irrigation District Administration Bureau，Inner Mongolia Linhe, China
国际会议
广州
英文
1-4
2011-05-13（万方平台首次上网日期，不代表论文的发表时间）