Prediction of Ambient Air Quality Based on Neural Network Optimized by Artificial Bee Colony Algorithm
LIU Dujin
PU Guolin
WANG Guangqiong
Abstract：Environmental air quality prediction plays an important role in the prevention of environmental pollution.Because the prediction ambient air quality is affected by many factors,the accuracy of prediction can not meet the needs of the development. The artificial bee colony algorithm(ABC)is improved and introduced into the back propagation neural network(BP).The reciprocal of the training error is used as the fitness function,and the initial value of the ABC is assigned as the initial weight and the threshold of BP.The global optimal solution obtained by the improved artificial bee colony algorithm(IABC)is the global optimal weight and threshold of the BP.The optimized BP neural network is used to predict the ambient air quality,by comparing the traditional BP neu-ral network,the traditional artificial bee colony optimization back propagation neural network.Experimental results show the opti-mized BP neural network proposed in this paper has achieved ideal results in ambient air quality prediction,and can be used in practice.
Keywords：artificial bee colony algorithmback propagation neural networkambient air quality predictionfitness function
Publication Date：2018-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 639-643 )