Orthogonal design and least squares wavelet support vector machine are integrated to optimize the technological parameters of hydro-mechanical deep drawing process using FEM.
在有限元模拟基础上，采用正交设计与最小二乘小波支持向量机对充液拉深过程参数优化进行了研究。
Classifying myoelectric signals using hidden Markov model and support vector machine to process myoelectric signals, with the task of discrimination five classes of multifunction prosthesis movement.
利用隐马尔克夫模型与支持向量机相结合，对站立和行走过程中的下肢表面肌电信号进行分类，用来控制多功能假肢。
The initial inverse model of process is built based on least squares support vector machine, and the Numbers of support vector is reduced through pruning algorithm.
初始过程的逆模型，建立了基于最小二乘支持向量机，并通过剪枝算法将支持向量的数量减少。
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