为了提高支持向量机(SVM)的识别性能，提出了在常用内核的基础上构造一个组合内核函数，然后用拟牛顿算法对其超参数进行优化的方法。

To improve the performance of support vector machines (SVM), a hybrid kernel is constructed from the existing common kernels, and the hyper-parameters are optimized by using a quasi-Newton method.

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采用遗传算法对某试验超燃冲压模型发动机的燃烧室构型参数进行了优化设计，所得方案的性能大大优于实际方案。

Scramjet combustor design optimization for a test scramjet model engine is conducted by employing genetic algorithms, and an optimal design much better than original design is obtained.

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利用超核实验数据来优化选择YN相互作用参数是有可能做得到的。

Utilizing experimental data, it is possible to optimize model parameters in the theoretical studies of YN interaction.

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