摘要
针对空气质量预测,提出了基于动态粒度小波神经网络的预测方法。为了选取合适的粒度,结合实际问题采用不断尝试的方法动态选取最优粒度,在最优粒度空间中求解问题。粒度变换后可以改变空气质量预测问题的求解空间,提高预测的精确度。实验也验证了动态选取的最优粒度作为小波神经网络的输入进行空气质量预测,可以取得更好的预测准确率。
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 granu- lating 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.
出处
《计算机工程与应用》
CSCD
2013年第6期221-224,共4页
Computer Engineering and Applications
基金
国家自然科学基金(No.61175046
No.61073117)
安徽高校省级自然科学研究项目(No.KJ2012Z121)
安徽师范大学人才培育基金项目(No.2010rcpy037)
关键词
商空间
动态粒度
小波神经网络
空气质量
预测
quotient space
dynamic granula
wavelet neural network
air quality
forecasting
