改进的BP神经网络算法在水质监测中的应用

Application of an Improved BP Neural Network Algorithm in Water Quality Monitoring

针对一类多输入多输出系统进行辨识,以"A simulation of the western basin of Lake Erie"为例,通过分析河流湖泊的水质特征,针对伊利湖湖泊水质建立数学模型,由于该环境系统为多输入多输出系统,文章采用了一种改进的BP神经网络算法,利用Matlab神经网络工具箱进行数据分析,绘出实际输出与模型输出的曲线以分析相关情况,检验建立的模型对于系统的辨识水平,给出传统BP网络和改进BP网络对该系统辨识的结果进行分析对比.文章还对不同噪声层次下的数据进行分析比较,并研究白噪声对于人工神经网络模型的影响.

This paper proposes an identification for a class of MIMO system. Taking ＂A simulation of the western basin of Lake Erie＂ as an example, quality characteristics of water system is analyzed and mathematical models of Lake Erie is made in this paper. An optimized BP ANN model is used for this MIMO system and the MATLAB＇s NNT is used to carry on data processing. The effectiveness of system identification is inspected by the curves between models＇ output and actual results. The comparison between traditional and optimized BP ANN is given at the end of this paper. In this paper data collected under different noises is compared to study on the effect of white noises on ANN.