Optimization And Application Of Water Quality Assessmental Model Based On Genetic Algorithms And Artificial Neural Networks

Based on a summary of water quality assessmental model's study,this paper tries to establish GA-BP neural network model,which combines Artificial Neuarl Network(ANN)and Genetic Algorithms(GA). Firstly,the paper clarifies the basic principle and the study process of BP neural network and Genetic Algorithms,analyses the existing weakness and introduces some general improvement measure. Secondly,making use of excellent global searching ability of GA and fine learning ability of ANN,we design the water quality assessmental model based on GA-BP algorithms. This model not only uses GA to optimize initial weights of neural network but also uses an arithmetic based on golden sectiong theory to optimize the amount of nodes in network's covert layer. These make training error of the model got to minimal value after less times cyclic iteration. In a sense,local optimizing problems,which is widely existed in neural network model training,can be overcome. Finnally,based on the improved arithmetic,GA-BP model for water quality assessment was built,and the water environmental monitoring results of a reservoir was put into model. We validate the model and compare with the simulation of other general method by applying MATLAB software to simulation. Result indicates that this algorithm has a better stability,precision and robustness .It shows that the result has highly consistency to the water quality and the method of this thesis is more reasonable.