改进的QGA-BP模型在复杂水质预测中的应用  

Application Research on Complex Water Quality Prediction with Improved QGA-BP Model

作　　者：于 汪家权[1] 

机构地区：[1]合肥工业大学管理学院,合肥230009 [2]安徽理工大学计算机科学与工程学院,淮南232001

出　　处：《模式识别与人工智能》2012年第4期705-708,共4页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金(No.50379003);安徽省自然科学基金(No.03045306)资助项目

摘　　要：水质预测是实现非线性水系统的柔性管理、防治水污染的前提工作.机理性水质预测模型的构建往往较复杂并且需要大量运算与数据,预测效果有时不够精确,其进一步推广应用也受到限制.文中以淮河复杂水环境非机理性水质预测为目的,构建改进的量子遗传算法优化BP神经网络模型,采用动态改进策略和灾变策略作为进化操作准则来优化BP模型的权值和阈值,用历史观测数据作为学习范例训练模型.对比实验结果发现,模型改进以后,进化代数、收敛速度和预测结果的准确率有较大提高.该模型用于水质预测的黑箱问题是可行的,拓展水环境管理的思路.Water quality prediction is a prerequisite for planning and managing of water environment and integrated controlling of water pollution. However, the construction of mechanism models is complex, the massive computation and data are required, prediction effects are not accurate enough and the further application of mechanism models is difficult. An improved QGA-BP model is constructed for predicting water quality of complex Huaihe river. The dynamic improvement strategy and catastrophe strategy are used as evolutionary operation guidelines in QGA to optimize the weight and the threshold of BP model. The past observation data are applied as the learning stances to train the model. The comparison of experimental results show that the evolution generation, convergence speed and prediction precision of the improved QGA-BP model are improved. The model is applicable to solve the black box problem of water prediction and provides a new way for water environment management.

关 键 词：改进的量子遗传算法优化BP神经网络(QGA—BP)模型 水质预测 动态改进策略 灾变策略 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]