基于灰色遗传BP神经网络的校园区间需水预测研究
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摘要
水资源预测是城市安全用水的基础保障,而校园用水预测是城市用水规划和管理的组成部分。针对校园用水受很多因素影响产生的不确定性,提出了基于灰色遗传BP神经的校园用水预测模型。模型对校园用水的数据进行灰色关联分析,并加入遗传算法去优化BP神经网络,经过残差计算,输出区间的预测值。运用该模型可以充分提取小样本信息,解决神经网络无法自动寻优的问题。通过Matlab对校园的用水区间数据进行仿真,得出的结果显示,预测的数据和实际数据基本吻合,其仿真精度可以达到90. 32%,验证了该方法的可行性,此预测方法有一定的借鉴意义。
Water resource prediction is the basic guarantee of urban safe water use,while campus water use prediction is the basis of urban water use planning and management. Aiming at solving the uncertainty of campus water consumption caused by several factors,a prediction model of campus water consumption based on grey genetic BP neural network is proposed. The model carries out grey relational analysis on the data of campus water consumption,and adds genetic algorithm to optimize BP neural network. After residual calculation,the predicted value of the output interval is obtained. This model can fully extract information from small samples and add genetic optimization neural network to solve the problem that neural network cannot automatically optimize. The results of Matlab simulation of campus water use interval data show that the predicted data and actual data are basically consistent with a simulation accuracy90. 32%,and it verified the feasibility of the method,the prediction method has some reference significance.
Water resource prediction is the basic guarantee of urban safe water use,while campus water use prediction is the basis of urban water use planning and management. Aiming at solving the uncertainty of campus water consumption caused by several factors,a prediction model of campus water consumption based on grey genetic BP neural network is proposed. The model carries out grey relational analysis on the data of campus water consumption,and adds genetic algorithm to optimize BP neural network. After residual calculation,the predicted value of the output interval is obtained. This model can fully extract information from small samples and add genetic optimization neural network to solve the problem that neural network cannot automatically optimize. The results of Matlab simulation of campus water use interval data show that the predicted data and actual data are basically consistent with a simulation accuracy90. 32%,and it verified the feasibility of the method,the prediction method has some reference significance.
引文
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[16]于增知,于宏兵,郑力燕.松花江流域冷水鱼潜在分布预测模型研究[J].水资源与水工程学报,2017,28(3):48-54.
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[18]王庆杰,岳春芳,李艺珍.基于MAGA-PPC模型的水资源配置方案综合评价[J].水资源与水工程学报,2018,29(3):105-110.
[2]张子博,刘玉明.公共建筑节水项目外部性研究———以北京某高校为例[J].水资源与水工程学报,2018,29(3):130-137.
[3]崔青华,夏井新.基于ARIMA-GARCH模型的城市主干道行程时间时变置信区间预测(英文)[J]. Journal of Southeast University(English Edition),2014,30(3):358-362.
[4]杜懿,麻荣永.不同改进灰色模型在广西年用水量预测中的应用研究[J].水资源与水工程学报,2017,28(3):87-90.
[5]李晓英,苏志伟,田佳乐,等.基于GRA-MEA-BP耦合模型的城市需水预测研究[J].水资源与水工程学报,2018,29(1):50-54.
[6]宋帆,杨晓华,武翡翡,等.灰色关联-集对聚类预测模型在吉林省用水量预测中的应用[J].水资源与水工程学报,2018,29(3):28-33.
[7]查木哈,卢志宏,翟继武,等.双隐含层BP神经网络模型在老哈河水质预测中的应用[J].水资源与水工程学报,2018,29(2):56-61.
[8]LI Jichao,ZHAO Danling,GE Bingfeng,et al. A link prediction method for heterogeneous networks based on BP neural network[J]. Physica A:Statistical Mechanics and its Applications,2018,495:1-17.
[9]王乃珍,王福田.基于灰色区间预测模型的轨道不平顺状态预测[J].铁路计算机应用,2015,24(1):1-3+7.
[10]侯珺,周金龙,曾妍妍,等.石河子地区地下水“三氮”空间分布特征及影响因素分析[J].水资源与水工程学报,2018,29(1):1-8.
[11]SUN Guidong,GUAN Xin,YI Xiao,et al. Grey relational analysis between hesitant fuzzy sets with applications to pattern recognition[J]. Expert Systems With Applications,2018,92:521-532.
[12]吴梦烟,杨侃,刘朗,等.基于灰色局势决策和组合赋权法的再生水评价[J].水资源与水工程学报,2018,29(3):111-117.
[13]高颖,方国华,林泽昕.投影寻踪和BP网络在农村饮用水水质评价中的应用[J].水资源与水工程学报,2017,28(2):115-119.
[14]时佳,薛联青,陈新芳,等.基于综合赋权法的叶尔羌河流域水资源承载力可变模糊综合评价[J].水资源与水工程学报,2017,28(5):32-36.
[15]杨红霞,蔡昕.基于WDO-PP模型的区域水资源系统与经济社会生态系统协调度评价[J].水资源与水工程学报,2017,28(2):68-75.
[16]于增知,于宏兵,郑力燕.松花江流域冷水鱼潜在分布预测模型研究[J].水资源与水工程学报,2017,28(3):48-54.
[17]刘佩瑶,郝振纯,王国庆,等.新安江模型和改进BP神经网络模型在闽江水文预报中的应用[J].水资源与水工程学报,2017,28(1):40-44.
[18]王庆杰,岳春芳,李艺珍.基于MAGA-PPC模型的水资源配置方案综合评价[J].水资源与水工程学报,2018,29(3):105-110.