摘要
针对工厂污水水质评价准确性低、实时性差等问题,提出一种将改进遗传算法(GA)和BP神经网络相结合对工厂污水的水质进行评价的方法。GA-BP神经网络不仅具有BP神经网络的非线性映射能力,还具有遗传算法的全局搜索能力。采用自适应算法对GA的交叉率和变异率进行改进,用GA优化BP的权值和阈值,将最优权值和阈值送给BP神经网络进行训练、预测,并与传统BP进行比较。实验结果表明,改进的GA-BP神经网络无论是收敛性、准确性还是实时性都优于传统BP网络。该方法用于污水水质评价具有应用推广价值。
A water quality evaluation method of factory sewage is proposed to solve the problems of low accuracy and poor timeliness of factory sewage quality assessment,which is based on the combination of improved GA and BP neural network. The GA-BP neural network has the nonlinear mapping ability of BP neural network,and global search ability of genetic algorithm.The adaptive algorithm is used to improve the crossover rate and mutation rate of GA. The GA is used to optimize the weight and threshold of BP neural network. The optimal weight and threshold are sent to BP neural network for training,prediction and comparison with traditional BP neural network. The experimental results show that the convergence,accuracy and timeliness of the improved GA-BP neural network are better than those of traditional BP neural network. The method has a certain application and promotion value for water quality evaluation of sewage.
作者
诸飞
俞阿龙
ZHU Fei;YU Along(School of Physics and Electronic-Electrical Engineering,Ningxia University,Yinchuan 750021,China;School of Physics and Electronic Electrical Engineering,Huaiyin Normal University,Huai’an 223300,China)
出处
《现代电子技术》
北大核心
2018年第11期133-138,共6页
Modern Electronics Technique
基金
国家自然科学基金资助项目(61350008)
江苏省高校产业化推进项目(JHB2012-55)
国家星火计划(2012GA690166)~~
关键词
工厂污水
水质分类
改进GA
BP神经网络
污水监测
自适应算法
factory sewage
water quality classification
improved GA
BP neural network
sewage monitoring
adaptive algorithm
