1) municipal daily water consumption
城市日用水量
1.
Combined with the characteristics and variation rule of the factors influencing municipal daily water consumption,a forecast model for municipal daily water consumption was set up,then the particle swarm optimization was used to optimize BP artificial neural network to solve the model.
结合城市日用水量影响因素的特点和变化规律,建立了城市日用水量预测模型,采用粒子群优化算法优化BP人工神经网络的连接权值,以求解该预测模型。
2.
Forecast model for municipal daily water consumption and its influencingfactors was set up, and then radial basis function ( RBF) network and support vector machines ( SVM) were adopted to solve the model.
结合城市日用水量影响因素的特点和变化规律,分析探讨了城市日用水量预测模型的求解方法。
2) urban water use
城市用水量
1.
Forecasting urban water use by combining multiple linear regression with grey model(1,1);
多元线性回归与GM(1,1)模型耦合预测城市用水量
2.
The principal component regression(PCR) is applied to set up the urban water use model,it can solve the problem of interactive correlation among the independent variables.
运用多元统计分析中的主成分分析法通过提取两个主成分,克服变量之间的相关性,然后再进行回归,建立主成分回归的城市用水量模型,并对模型进行了拟合检验,取得了较为满意的结果。
3) urban water consumption
城市用水量
1.
BP model for forecasting urban water consumption;
城市用水量BP网络预测模型
2.
The chaotic characters of time series of urban water consumption were judged using the chaos theory.
采用混沌理论对城市用水量时间序列的混沌特性进行了判定。
3.
The improved method was compared with the traditional method in the case of short-term forecasting for urban water consumption.
通过对城市用水量短期预测的实例研究,将改进算法与传统算法进行比较。
4) city water consumption
城市用水量
1.
Forecast research of city water consumption which based on genetic NN;
基于遗传神经网络的城市用水量预测研究
2.
This article applied the Multiple Linear Regressions to forecast city water consumption.
本文应用多元回归的方法,对城市用水量进行预测。
3.
This article applies the multiplelinearregression to forecast city water consumption.
应用多元回归的方法,对城市用水量进行预测。
5) urban water demand
城市用水量
1.
Forecast model for urban water demand using artificial neural network;
人工神经网络法预测城市用水量
6) city industrial water quantity
城市工业用水量
1.
GM(1,1) model is used for forecasting city industrial water quantity.
灰色GM(1,1)是预测城市工业用水量的模型,这种模型不适合长期的、随机和波动性较大的数据序列预测,但是马尔可夫模型适合描述随机波动性较大的预测问题。
补充资料：用日陶瓷
分子式：
CAS号：
性质：生活用的陶瓷器皿。包括饮食用具，厨房用具，贮藏物品用具和日常生活用的其他陶瓷器具。按种类分，日用陶瓷包含有瓷器、陶瓷、精陶和炻器等。
CAS号：
性质：生活用的陶瓷器皿。包括饮食用具，厨房用具，贮藏物品用具和日常生活用的其他陶瓷器具。按种类分，日用陶瓷包含有瓷器、陶瓷、精陶和炻器等。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条