针对不同空间距离和色调障碍物，建立距离判识结果随速度变化的一元线性回归模型及BP神经网络模型；
Study laws of distance discriminating results to red and green obstacles change with velocity at different depths distance and establish a linear regression model and BP neural network model.
利用回归分析法，建立一元线性回归处理的数学模型和多元线性回归处理的数学模型。
With regression analysis, a mathematics model of both simple linear and multiple linear regressions were established.
根据实测的数据，采用一元线性、多元线性和非线性回归进行拟合，得到34个红海榄幼苗主要形态因子和生物量的回归模型。
Based on the data observed, 34 regression models on the morphological variables and biomass of the seedlings were set up using linear, multilinear and non linear regression.
即把一元非线性回归和多元线性回归结合起来，构造一个混合回归模型，这样就减小了模型的残差平方和，从而提高预报的准确性。
We combine unitary nonlinear regression with multivariate linear regression, it can reduce the error sum of squares of the model and improve the precision of forecast.
第六部分采用一元线性回归、多项式回归、灰色系统G（1，1）模型和组合预测模型对山东省现代物流业未来需求状况进行了中期预测。
The sixth part forecasts the future logistics demand of Shandong Province on the basis of one linear regression, polynomial regression, gray system G(1,1) model and combination forecast model.
第六部分采用一元线性回归、多项式回归、灰色系统G（1，1）模型和组合预测模型对山东省现代物流业未来需求状况进行了中期预测。
The sixth part forecasts the future logistics demand of Shandong Province on the basis of one linear regression, polynomial regression, gray system G(1,1) model and combination forecast model.
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