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시스템잡음에 강건한 SOM-TVC 기법을 이용한 근전도 패턴 인식에 관한 연구
A Study on the EMG Pattern Recognition Using SOM-TVC Method Robust to System Noise
- 김인수;
- 이진;
- 김성환
초록
This paper presents an EMG pattern classification method to identify motion commands for the control of the artificial arm by SOM-TVC(self organizing map - tracking Voronoi cell) based on neural network with a feature parameter. The eigenvalue is extracted as a feature parameter from the EMG signals and Voronoi cells is used to define each pattern boundary in the pattern recognition space. And a TVC algorithm is designed to track the movement of the Voronoi cell varying as the condition of additive noise. Results are presented to support the efficiency of the proposed SOM-TVC algorithm for EMG pattern recognition and compared with the conventional EDM and BPNN methods.
키워드
EMG Pattern Recognition; Neural Network; Eigenvalue; Additive Nnoise and SOM-TVC; EMG Pattern Recognition; Neural Network; Eigenvalue; Additive Nnoise and SOM-TVC
- 제목
- 시스템잡음에 강건한 SOM-TVC 기법을 이용한 근전도 패턴 인식에 관한 연구
- 제목 (타언어)
- A Study on the EMG Pattern Recognition Using SOM-TVC Method Robust to System Noise
- 저자
- 김인수; 이진; 김성환
- 발행일
- 2005-06
- 유형
- Y
- 저널명
- 전기학회논문지 D권
- 권
- 54
- 호
- 6(D)
- 페이지
- 417 ~ 422