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- W2372853904 abstract "Electromyographic signals have the characteristics of non-stationary,random and nonlinear,therefore,this paper proposed an EMG signal recognition model based on genetic support vector machine identification.Wavelet transform was adopted to extract the EMG features,and then genetic algorithm was used to select the optimal features to reduce feature dimension.The parameters of support vector machine were optimized by genetic algorithm in order to improve the recognition rate of EMG signals.The simulation results show that the proposed model improves the average recognition rate of EMG signals,and the recognition results are very stable and good robustness,which provides a new recognition method for EMG signals and other no stationary biopotential signals." @default.
- W2372853904 created "2016-06-24" @default.
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- W2372853904 date "2013-01-01" @default.
- W2372853904 modified "2023-09-26" @default.
- W2372853904 title "Research on Feature Extraction and Classification of Electromyographic Signals Recognition" @default.
- W2372853904 hasPublicationYear "2013" @default.
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