小波神经网络在北京河湖水华预测中的应用  

Water-bloom forecasting in lakes of Beijing based on wavelet artificial neural network

作　　者：吴巧媚[1] 刘载文[1] 王小艺[1] 崔莉凤[2] 连晓峰[1] 许继平[1] 

机构地区：[1]北京工商大学信息工程学院,北京100037 [2]北京工商大学化学与环境工程学院,北京100037

出　　处：《计算机工程与应用》2010年第12期233-235,共3页Computer Engineering and Applications

基　　金：北京市自然科学基金重点项目No.8101003;北京市高校人才强教计划项目(No.PHR201007123;No.PHR201008238);北京市科技创新平台项目;北京市新世纪百千万人才工程资助项目~~

摘　　要：针对不同季节水华生长的不同特点,在对水华生长规律研究的基础上,运用小波分析对表征水华的叶绿素信号进行降噪处理,建立一种结合小波变换与神经网络相结合的水华预测模型(WANN模型),该模型既有神经网络的自学习能力特性,又有小波的局部特性,并将其应用到北京夏季河湖水华预测中。通过小波多分辨率分析,对样本包含的信息进行充分挖掘,提取反映其变化规律的成分,有效避免了原始数据中噪声对网络的干扰,提高网络的性能,WANN模型预测结果与BP网络预测结果对比,具有较高的预测能力,从而获得相对理想的预测效果。Aiming at the different characteristics of water-bloom growing in different seasons,a predicting model combined with wavelet analysis and artificial neural network（WANN） is proposed based on the study in mechanism of water-bloom growing.The model which has great capabilities of both self-study of artificial neural network and multi-resolution analysis power of wavelet,is applied in forecasting water-bloom in lakes and rivers of Beijing in summer.Through the wavelet multi-resolution analysis,the influence is decreased efficiently from unnecessary noise brought by primary data,finally the performance of network is enhanced. Compared with the calculation results,WANN possesses high forecasting accuracy and has better performance than BP model.

关 键 词：水华预测 小波分析 神经网络 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]