人民黄河
人民黃河
인민황하
Yellow River
2014年
3期
46-48
,共3页
改进证据理论%灰色神经网络%水质%预测
改進證據理論%灰色神經網絡%水質%預測
개진증거이론%회색신경망락%수질%예측
improved evidence theory%gray neural network%water quality%prediction
针对水质预测过程中样本数据少的特点，引入了改进证据理论和灰色神经网络相结合的组合预测方法。首先利用灰色神经网络作为单一模型对水质进行初步预测，再用神经网络对预测结果进行分析建模，得到每个单一预测模型的可信度，最后采用改进证据理论进行融合决策，以获得各单一预测模型的权重，从而实现了水质的组合预测。实例分析结果表明，该方法拟合误差小、预测精度高。
針對水質預測過程中樣本數據少的特點，引入瞭改進證據理論和灰色神經網絡相結閤的組閤預測方法。首先利用灰色神經網絡作為單一模型對水質進行初步預測，再用神經網絡對預測結果進行分析建模，得到每箇單一預測模型的可信度，最後採用改進證據理論進行融閤決策，以穫得各單一預測模型的權重，從而實現瞭水質的組閤預測。實例分析結果錶明，該方法擬閤誤差小、預測精度高。
침대수질예측과정중양본수거소적특점，인입료개진증거이론화회색신경망락상결합적조합예측방법。수선이용회색신경망락작위단일모형대수질진행초보예측，재용신경망락대예측결과진행분석건모，득도매개단일예측모형적가신도，최후채용개진증거이론진행융합결책，이획득각단일예측모형적권중，종이실현료수질적조합예측。실례분석결과표명，해방법의합오차소、예측정도고。
Aiming to the characteristics of few sample data in water quality prediction process,this paper introduced a combination prediction meth-od based on improved evidence theory combining with gray neural network. Firstly,the gray neural network was used as single model to preliminary predict the water quality;then the neural network was used to analyze the predictive results and establish models to get the credibility of every single prediction model;finally,the evidence theory was employed to fuse them and to obtain the single prediction mode1 weight. Hence,the water quali-ty prediction was realized. The results show that the method has small fitting error and high prediction precision.
