计算机工程与应用2013，Vol.49Issue(6)：221-224,4.DOI:10.3778/j.issn.1002-8331.1210-0277
基于动态粒度小波神经网络的空气质量预测
Air quality forecasting based on dynamic granular wavelet neural network
摘要
Abstract
A new method of air quality forecasting based on dynamic granular wavelet neural network is put forward by the combination of quotient space theory, wavelets theory and neural network theory. Different granula can be obtained by the granulating of original data domain using quotient space theory and the best one can be found by testing it in the practice. The best guanula is used as the input to wavelet neural network for air quality forecasting. By this means, the forecast accuracy can be improved after the problem solving space has been changed. Experimental result of air quality forecasting also shows that this method is more effective.关键词
商空间/动态粒度/小波神经网络/空气质量/预测Key words
quotient space/ dynamic granula/ wavelet neural network/ air quality/ forecasting分类
信息技术与安全科学引用本文复制引用
汪小寒,张燕平,赵姝,张铃..基于动态粒度小波神经网络的空气质量预测[J].计算机工程与应用,2013,49(6):221-224,4.基金项目
国家自然科学基金(No.61175046,No.61073117) （No.61175046,No.61073117）
安徽高校省级自然科学研究项目(No.KJ2012Z121) （No.KJ2012Z121）
安徽师范大学人才培育基金项目(No.2010rcpy037). （No.2010rcpy037）
