西南师范大学学报(自然科学版)
西南師範大學學報(自然科學版)
서남사범대학학보(자연과학판)
JOURNAL OF SOUTHWEST CHINA NORMAL UNIVERSITY
2014年
9期
127-132
,共6页
GRNN模型%布谷鸟算法%城市需水量%预测
GRNN模型%佈穀鳥算法%城市需水量%預測
GRNN모형%포곡조산법%성시수수량%예측
GRNN model%cuckoo search algorithm%urban water demand%prediction
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。
為瞭更加準確地預測城市需水量,提齣一種基于改進佈穀鳥算法優化廣義迴歸神經網絡模型的城市需水量預測方法。該方法採用改進的佈穀鳥算法對廣義迴歸神經網絡的平滑因子進行優化,建立改進佈穀鳥算法優化的廣義迴歸神經網絡模型(ICS-GRNN),併應用于南寧市城市需水量預測中。通過使用南寧市2001-2012年城市需水量測試數據分彆對傳統GRNN法和ICS-GRNN法的預測結果進行比較,結果錶明,該方法具有更高的預測精度和數據擬閤能力。
위료경가준학지예측성시수수량,제출일충기우개진포곡조산법우화엄의회귀신경망락모형적성시수수량예측방법。해방법채용개진적포곡조산법대엄의회귀신경망락적평활인자진행우화,건립개진포곡조산법우화적엄의회귀신경망락모형(ICS-GRNN),병응용우남저시성시수수량예측중。통과사용남저시2001-2012년성시수수량측시수거분별대전통GRNN법화ICS-GRNN법적예측결과진행비교,결과표명,해방법구유경고적예측정도화수거의합능력。
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 .