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| 改进的CS-GRNN模型在城市需水量预测中的应用 | |
| 引用本文： | 屈迟文[],、傅彦铭[],、戴俊[].改进的CS-GRNN模型在城市需水量预测中的应用[J].西南师范大学学报(自然科学版),2014,39(9):127-132. |
| 作者姓名： | 屈迟文[] 、傅彦铭[] 、戴俊[] |
| 作者单位： | 1. 百色学院数学与计算机信息工程系,广西百色,533000 2. 广西大学计算机与电子信息学院,南宁,530004 3. 百色学院经济管理系,广西百色,533000 |
| 基金项目： | 广西自然科学青年基金项目(2014GXNSFBA118283)；广西高校科研项目(2013YB247)． |
| 摘 要： | 为了更加准确地预测城市需水量,提出一种基于改进布谷鸟算法优化广义回归神经网络模型的城市需水量预测方法.该方法采用改进的布谷鸟算法对广义回归神经网络的平滑因子进行优化,建立改进布谷鸟算法优化的广义回归神经网络模型(ICS-GRNN),并应用于南宁市城市需水量预测中.通过使用南宁市2001—2012年城市需水量测试数据分别对传统GRNN法和ICS-GRNN法的预测结果进行比较,结果表明,该方法具有更高的预测精度和数据拟合能力. |
| 关 键 词： | GRNN模型 布谷鸟算法 城市需水量 预测 |
On Application of Generalized Regression Neural Network Potimized by Cuckoo Search Algorithm in Urban Water Demand Prediction |
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| QU Chi-wen,FU Yan-ming,DAI Jun.On Application of Generalized Regression Neural Network Potimized by Cuckoo Search Algorithm in Urban Water Demand Prediction[J].Journal of Southwest China Normal University(Natural Science),2014,39(9):127-132. | |
| Authors: | QU Chi-wen[] FU Yan-ming[] DAI Jun[] |
| Institution: | QU Chi-wen;FU Yan-ming;DAI Jun;Department of Mathematics &Computer Information Engineering,Baise University;Computer and Electronic Information College,Guangxi University;Department of Economics & Management,Baise University; |
| Abstract: | In order to forecast city water requirement accurately ,an urban water demand forecasting meth-ods with generalized regression neural network model based on the Improved Cuckoo Search algorithm (ICS-GRNN) has been proposed in this paper .The smoothing factor of generalized regression neural net-work is optimized by improved cuckoo search algorithm ,then ICS-GRNN model has been established and applied to predict the urban water demand of Nanning .The comparison between the ICS-GRNN and tradi-tional GRNN indicates that the new model has higher prediction accuracy ,stronger data capability of fit-ting by using the water consumption data in Nanning city from 2001 to 2012 . |
| Keywords: | GRNN model cuckoo search algorithm urban water demand prediction |
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