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中文摘要：
该文提出一种计算非线性时间序列信号Lyapunov指数的新方法一球均值Lyapunov指数计算法，用于肢体肌电信号的特征提取与分类。首先采用所提方法计算出肌电信号的Lyapunov指数，并与关联维组合构成输入特征向量，然后用二又树法构造基于对支持向量机的多类分类器，对握拳、展拳、腕内旋、腕外旋4类动作模式进行分类识别。实验结果表明，该方法不仅具有比Rosenstein算法更强的抗干扰能力，而且在肌电信号的特征提取与分类应用中取得96．0％以上的识别率，适合于分析信噪比较低的混沌信号。
英文摘要：
A new method named ball-averaged Lyapunov exponents method is presented to calculate Lyapunov exponents of nonlinear time-series signals. The method can be used as feature extraction and classification of electromyography. Firstly, the Lyapunov exponents of electromyography is calculated and it is combined with correlation dimension as input eigenvector. Then, multi-class classifier is constructed based on Twin Support Vector Machines （TSVM） with binary-tree. Finally, the four hand gestures （namely, radial flexion and ulnar flexion hand opening and closing） are classified. The experimental results show that the method has stronger anti-jamming capability than Rosenstein method, and the recognition rate is above 96.0% in feature extraction and classification of electromyography. The proposed method is suitable for analyzing chaotic signals with lower signal-to-noise ratio.
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