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。

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A new two-stage method of fingerprint classification is proposed that is based on hidden Markov model (HMM) and support vector machine (SVM).

提出了一种利用隐马尔可夫模型(HMM)和支持向量机(SVM)的两级指纹分类新方法。

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Then, three safety modeling methods of human-machine system are introduced: probability logic method, Markov process method and random parameter function method.

然后介绍了用于人机一体化系统安全性建模的三种数据方法：概率逻辑法、马尔柯夫过程法和随即参数函数法。

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