计算机与数字工程2018，Vol.46Issue(4)：639-643,5.DOI:10.3969/j.issn.1672-9722.2018.04.002
基于蜂群优化神经网络的环境空气质量预测
Prediction of Ambient Air Quality Based on Neural Network Optimized by Artificial Bee Colony Algorithm
摘要
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.关键词
人工蜂群算法/反向传播神经网络/环境空气质量预测/适应度函数Key words
artificial bee colony algorithm/back propagation neural network/ambient air quality prediction,fitness function分类
信息技术与安全科学引用本文复制引用
刘笃晋,蒲国林,王光琼..基于蜂群优化神经网络的环境空气质量预测[J].计算机与数字工程,2018,46(4):639-643,5.基金项目
国家自然科学基金项目(编号:61152003) （编号:61152003）
四川省教育厅重点项目(编号:16ZA0353),四川省教育厅基金项目(编号:16ZB0360),四川文理学院2015年度特色培育一般项目(编号:2015TP001Y)资助. （编号:16ZA0353）
