1) daily water consumption
日用水量
1.
Equal-dimension and new information SVR forecasting model of urban daily water consumption;
城市日用水量需求预测的等维新息SVR建模方法
2.
A nonlinear combined regression model for predicting daily water consumption was developed by introducing history data of daily water consumption in time series into regression analysis model using temperature and other parameters as variables.
将时间序列中的日用水量历史数据引入以温度等作变量的回归分析模型,建立了日用水量非线性回归组合预测模型,同时为进一步提高预测精度,用4阶自回归模型对回归残差序列进行时间序列分析,建立了日用水量预测实用动态组合模型。
2) water demand
日用水量
1.
The nonparametric multi regression model of urban daily water demand;
城市日用水量预测的非参数模型研究
2.
According to the effecting factor and the characteristic of urban daily water and the error of line model,we set up the nonparametrie multi regressive model of daily water demand.
根据城市用水量的影响因素及特点,针对传统的线性回归模型误差较大的缺点,基于核估计与局部线性估计理论,建立了城市日用水量的非参数回归预测模型。
3) coal washery daily water consumption
选煤厂日用水量
1.
Applied a novel machine learning algorithm-least squares support vector machines(LSSVM) into coal washery daily water consumption times series prediction.
针对选煤厂日用水量时间序列的预测问题,提出应用最小二乘支持向量机(LSSVM)这一新的机器学习方法来实现日用水量的短期预测。
4) 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。
5) forecasting of daily water consumption
日用水量预测
1.
The application of BP-NN optimized by MCPSO for forecasting of daily water consumption
MCPSO优化BP-NN在日用水量预测中的应用
6) ever-increasing demand
日益增加的用水量
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