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基于灰色广义回归神经网络的工业废水排放量预测
引用本文:张文丽,路金喜,宋双虎,董淑惠,关珂. 基于灰色广义回归神经网络的工业废水排放量预测[J]. 水资源与水工程学报, 2007, 18(1): 64-67
作者姓名:张文丽  路金喜  宋双虎  董淑惠  关珂
作者单位:1. 河北农业大学,城乡建设学院,河北,保定,071001
2. 张河湾抽水蓄能电站,河北,石家庄,050001
3. 华北电力大学,河北,保定,071000
摘    要:将GM(1,1)预测模型与广义回归神经网络结合起来,构建了一种新型串联灰色神经网络预测方法,有效地将灰色系统的贫乏数据建模和神经网络特有的非线性适应性信息处理能力相融合,充分提取历史数据及相关因素数据包含的信息,建立精度较高的预测模型。通过对工业废水排放量实例预测,结果表明该方法是有效可行的。

关 键 词:改进的灰色模型  广义回归神经网络  相关因素数据  工业废水排放量预测
文章编号:1672-643X(2007)01-0064-04
修稿时间:2006-09-03

Forecast of industrial waste water volume based on GM-GRNN
ZHANG Wen-li,LU Jin-xi,SONG Shuang-hu,DONG Shu-hui,GUAN Ke. Forecast of industrial waste water volume based on GM-GRNN[J]. Journal of water resources and water engineering, 2007, 18(1): 64-67
Authors:ZHANG Wen-li  LU Jin-xi  SONG Shuang-hu  DONG Shu-hui  GUAN Ke
Abstract:A new series grey ANN forecast model was proposed by unified the GM(1,1) with GRNN,effectively integrated the Grey System that can be constructed the forecast model with poor information and the GRNN was capable of processing non-linear adaptable information,so the new model had both of their advantages.It be fully considered the historic data and correlation factor data,the forecasting results were high precision.An example of industrial waste water volume was forcasted,the results have shown that this method was effective and feasible.
Keywords:improved GM(1  1)  GRNN  correlation factor data  forecast of industrial waste water volume
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