给出带有模糊决策的模糊机会约束规划模型，在此基础上，研究模糊线性支持向量分类机(算法)和模糊线性支持向量回归机(算法)。
Proposed the model of fuzzy chance constrained programming with fuzzy decision, and did some research on fuzzy linear support vector regression (algorithm) on this base.
通过线性规划技术和采用尺度函数作为核函数来实现支持向量回归模型。
Using linear programming technique and scaling kernel function, the support vector regression model was obtained.
利用支持向量回归的方法对非线性过程进行建模，采用预测函数控制方法进行控制。
The support vector regression method is used for modeling the nonlinear process, and the predictive functional control method is used to control.
提出了一种基于分类技术的支持向量回归方法，解决数据分布未知、数学模型未知的非线性回归问题。
A support vector regression method based on classification is presented to solve the nonlinear regression problem with unknown data distribution and mathematical model.
提出一种基于支持向量回归机(SVR)的非线性动态系统建模方法。
A modeling method for nonlinear dynamic system based on Support Vector Regression (SVR) was proposed in this paper.
提出一种新的尺度核支持向量回归方法，并应用于非线性系统辨识问题。
A new scaling kernel support vector regression was proposed for nonlinear system identification problem.
同时针对神经网络易于陷入局部极值、结构难以确定和泛化能力较差的缺点，引入了能很好解决小样本、非线性和高维数问题的支持向量回归机来进行油气田开发指标的预测；
The method of support vector regression which can well resolve the problem with the insufficient swatch, nonlinear and high dimension is introduction to predict the development index of gas-field.
用支持向量回归（SVR）的方法分析和预测时间序列，可解决复杂非线性系统的建模问题。
The Support Vector Regression(SVR)is used for the time series analysis and prediction to resolve the complex nonlinear system modeling problems.
用支持向量回归（SVR）的方法分析和预测时间序列，可解决复杂非线性系统的建模问题。
The Support Vector Regression(SVR)is used for the time series analysis and prediction to resolve the complex nonlinear system modeling problems.
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