非线性回归分析 meaning in Chinese
nonlinear regression analysis
Examples
- Finally , on the basis of non - linear return fit , a mathematics model of the tested members " m - curve is fitted , for working out the local standard of chongqing
最后，在采用非线性回归分析的基础上，拟合出一个m曲线的数学模型，供编制重庆市地方标准《轻型钢结构住宅技术规程》参考。 - According to the experiments data , multi - variables nonlinear regressions were put to use , and the influencial behavior of three dimensional printing ' s product to the different gypsum based powder ' s mixtures was validated
对试验数据进行多元非线性回归分析，验证了石膏基粉末中各个成分对三维打印成形制件的影响规律。 - When the water - air ratio increases , air ' s changing quantity of enthalpy and absolute humid will be enhanced . at last , the paper analyses the data from single factor experiment by mathematics method and gets relationship formulas about the nozzle flux , valid humidifying quantity and air ' s enthalpy changing quantity
最后，对上述单因素试验得到的数据进行数学分析，运用线性回归分析、多项式回归分析和非线性回归分析的数学方法，得到影响撞针型高压小孔径离心式喷嘴的喷嘴流量、有效加湿量和空气焙变量的数学关系式。 - Sufficient test data were acquired for investigating the damage and constitutive behavior of concrete . second , according to the fitting of experimental data , the evolution equation of damage is obtained . the main factors affecting the damage development are examined and the causes are expounded
其次，通过对试验结果的非线性回归分析，得到损伤演化的经验表达式，并以此为依据，分别深入地研究了混凝土在经历过常规三轴受压和三向等压等荷载历史后，抗压强度和抗拉强度随荷载历史增加而发生劣化的演化规律。 - On the basis of analyzing historical water consumption in shenzhen , hourly water demand , daily water demand and annual water demand are studied using non - linear regression model , time series model , artificial neural network , gray model and compounding model , etc . by anglicizing merits and demerits of every model in different forecasts , time series model is appropriate to hourly water demand forecast ; compound forecasting model of time series and regress analysis is appropriate to daily water demand forecast ; gray model and regress analysis model is appropriate to annual water demand forecast
本文通过分析深圳特区用水量的变化规律，采用非线性回归分析、时间序列、人工神经网络、灰色模型和组合预测模型分别对时需水量、日需水量、年需水量进行了研究。通过比较分析各种模型在不同预测类型中的优缺点，时需水量预测较适合采用时间序列模型；日需水量预测较适合采用时序?回归分析组合预测模型；年需水量预测较适合灰色模型、回归分析模型；提出了指导选择城市需水量预测模型的方法。