激光杂志
激光雜誌
격광잡지
LASER JOURNAL
2014年
9期
92-95
,共4页
BP神经网络%AR模型%EMG信号%手指运动识别
BP神經網絡%AR模型%EMG信號%手指運動識彆
BP신경망락%AR모형%EMG신호%수지운동식별
BP Neural Network%AR Model%EMG Signal%Finger movement recognition
在基于肌电信号(EMG)手指运动的模式识别中,稳定性和识别率是两个主要问题,为此提出了一种新的EMG模式识别算法。该算法采用现代信号处理理论中的AR模型和改进的BP神经网络相结合的算法,有效的解决了BP网络识别中落入局部极值问题。进行试验,将提取到的特征值输入MATLAB建立一个改进多层BP神经网络,识别三个不同类型的手指运动。实验表明,改进BP算法较传统BP算法获得了更高的识别精度,达到94%左右。
在基于肌電信號(EMG)手指運動的模式識彆中,穩定性和識彆率是兩箇主要問題,為此提齣瞭一種新的EMG模式識彆算法。該算法採用現代信號處理理論中的AR模型和改進的BP神經網絡相結閤的算法,有效的解決瞭BP網絡識彆中落入跼部極值問題。進行試驗,將提取到的特徵值輸入MATLAB建立一箇改進多層BP神經網絡,識彆三箇不同類型的手指運動。實驗錶明,改進BP算法較傳統BP算法穫得瞭更高的識彆精度,達到94%左右。
재기우기전신호(EMG)수지운동적모식식별중,은정성화식별솔시량개주요문제,위차제출료일충신적EMG모식식별산법。해산법채용현대신호처리이론중적AR모형화개진적BP신경망락상결합적산법,유효적해결료BP망락식별중락입국부겁치문제。진행시험,장제취도적특정치수입MATLAB건립일개개진다층BP신경망락,식별삼개불동류형적수지운동。실험표명,개진BP산법교전통BP산법획득료경고적식별정도,체도94%좌우。
In the pattern recognition of Finger movement based on electromyography (EMG), the Stability and Recognition rate are both the problem. The paper proposes a new method of pattern recognition of EMG signal. The method combination of the algorithm using BP neural network AR model and the improvement of modern signal pro-cessing in the theory of the algorithm, can effectively solve the problem of BP network into local extremum recogni-tion. To make the classification of the eigenvalues of the EMG, these eigenvalues have been inputted to the MAT-LAB to build up a improved multilayer BP neural networks. For the recognition of three different kinds of finger mo-tion's EMG signals, the experiments show that the improved BP algorithm, to obtain higher recognition accuracy than the traditional BP algorithm, to around 94%.