在软测量建模过程中，基于支持向量机的算法能较好地解决小样本、非线性、高维数、局部极小点等问题。

In model establishment of soft-sensing, the problems of small sample, non-linearity, high dimensions and local minimal value can be well solved by support vector machine algorithm.

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关于算法分析的定理证明了这种混合算法对于紧致集内的权向量构成的任意连续函数能依概率1收敛于全局极小值。

It is shown that this algorithm ensures convergence to a global minimum with probability 1in a compact region of a weight vector space.

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给出了两个拓扑向量空间的乘积空间上截口定理，极小极大不等式及一个推广的不动点定理。

A section theorem, a minimax inequality and a generalized fixed point theorem where the underlying space is a product space of two topological vector Spaces, are given.

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