长江科学院院报2017，Vol.34Issue(9)：1-5,5.DOI:10.11988/ckyyb.20160520
耦合动态方程的神经网络模型在水质预测中的应用
Application of Neural Network Model Coupled with Dynamic Equationin Water Quality Prediction
摘要
Abstract
Precise prediction of water quality trend is of vital importance for water resources management.Commonly used data-driving models cannot reflect the physical characteristics of research objective.In view of this, a neural network coupled with dynamic equation is proposed in this paper, and the method to couple dynamic equation into model iteration is also given.A numerical case and a practical case are used to demonstrate the difference between network model with mechanism priori-knowledge and traditional network model.The results of fitting degree and calculation error indicate that the coupled priori-knowledge is able to improve calculation accuracy and enhance non-linear fitting.The proposed model is applicable and rational in water quality prediction.Sample size is the basis of neural network model application, and coupling mechanism priori knowledge under the circumstance of fixed sample size is an efficient approach to improving prediction accuracy.关键词
水质预测/神经网络模型/耦合动态方程/机理性先验知识/Mackey-Glass混沌系统Key words
water quality prediction/neural network model/dynamic equation/mechanism priori knowledge/Mackey-Glass chaotic system分类
资源环境引用本文复制引用
周彦辰,胡铁松,陈进,许继军,周研来..耦合动态方程的神经网络模型在水质预测中的应用[J].长江科学院院报,2017,34(9):1-5,5.基金项目
国家自然科学基金项目(71171151,51509008) （71171151,51509008）
湖北省自然科学基金项目(2015CFA157) （2015CFA157）
