Using linear programming technique and scaling kernel function, the support vector regression model was obtained.

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。

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The optimal objective value is a complicated piecewise linear function of the right-hand-side vector of the constraints, and its analytical expression is normally hard to obtain.

当线性规划约束条件的右端向量在一定范围内变化时，目标函数的最优值是右端向量的一个复杂的分片线性函数，但通常难以给出分析表达式。

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Then, by the function expansion, the nonlinear transfer function of this model was converted to intermediate model which is linear one and can be identified using support vector regression (SVR).

再利用函数展开将模型的非线性传递函数转换为等价的线性中间模型，并通过SVR求取中间模型参数。

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