关键词: 小波神经网络, 出水水质, KPCA核主元分析, 结构风险最小化

Abstract: On the basis of analyzing the classical methods of wastewater treatment plant effluent quality prediction,the paper puts forward a novel predictive method by Wavelet Neural Networks（WNN）. Firstly,the paper utilizes kernel principal component analysis method to realize reduce the dimension of the input vectors and orthogonalize the components of the input vectors.Then effluent quality predictive model is built using wavelet neural networks.The structure of WNN is trained using Structural Risk Minimization（SRM） based on statistical learning theory and weights of networks are optimized by adaptive orthogonal least square.The novel algorithm can ensure great probability for global optimization.The experiment result shows that the novel predictive model has higher precision and more flexibility.

Key words: Wavelet Neural Networks（WNN）, effluent quality, Kernel Principal Component Analysis（KPCA）, Structural Risk Mini-mization（SRM）