Back Propagation Neural Network in the Water Quality Evaluation of Qingdao Dagu River
Compared with other methods, NN (neural network) model in water quality evaluation had better performance in the application to water quality assessment. In this paper, In order to improve the NN models performance, arithmetic, determination of hidden layer nodes amount and the training samples were optimized. Gradient descending arithmetic added by momentum and self-adaptive learning rate was chosen. The amount of nodes in networks hidden layer was optimized by pilot calculation arithmetic based on empirical equation. Training samples was extended by random differential in critical value space of grades to improve models robustness and veracity of distinguishing. When it was used to evaluate the water quality of Qingdao Dagu river, the improved ANN (artificial neural network) model displayed a good performance.
water quality assessment neural network learning algorithm training sample
Miao Qun Li Yue Hai Yang He Hui Zhang Xiaomei
Qingdao University & Qingdao Technological University Qingdao, Shandong, China Qingdao Technological University Qingdao, Shandong, China
国际会议
上海
英文
2795-2797
2008-05-16（万方平台首次上网日期，不代表论文的发表时间）