지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국정보처리학회 JIPS(Journal of Information Processing Systems) JIPS(Journal of Information Processing Systems) 제15권 제2호
- 발행연도
- 2019.1
- 수록면
- 422 - 432 (11page)
이용수
초록· 키워드
오류제보하기
A hybrid kernel function of support vector machine is proposed to improve the classification performance ofpower quality disturbances. The kernel function mathematical model of support vector machine directly affectsthe classification performance. Different types of kernel functions have different generalization ability andlearning ability. The single kernel function cannot have better ability both in learning and generalization. Toovercome this problem, we propose a hybrid kernel function that is composed of two single kernel functions toimprove both the ability in generation and learning. In simulations, we respectively used the single and multiplepower quality disturbances to test classification performance of support vector machine algorithm with theproposed hybrid kernel function. Compared with other support vector machine algorithms, the improvedsupport vector machine algorithm has better performance for the classification of power quality signals withsingle and multiple disturbances.
목차
등록된 정보가 없습니다.
참고문헌 (16)
참고문헌 신청
W. G. Morsi / 2011 / Power quality evaluation in smart grids considering modern distortion in electric power systems / Electric Power Systems Research 81(5):1117~1123
Z. Liu / 2013 / Review of power quality mixed disturbances identification / Power System Protection and Control 41(13):146~153
D. De Yong / 2015 / An effective power quality classifier using wavelet transform and support vector machines / Expert Systems with Applications 42(15-16):6075~6081
Y. Wu / 2016 / Feature extraction method of power quality disturbance signals based on modified S-transform / Proceedings of the Chinese Society of Electrical Engineering 36(10):2682~2689
F. Xu / 2012 / Classification for power quality short duration disturbances based on generalized S-transform / Proceedings of the Chinese Society of Electrical Engineering 32(4):77~84
Z. Liu / 2013 / Review of power quality mixed disturbances identification / Power System Protection and Control 41(13):146~153
D. De Yong / 2015 / An effective power quality classifier using wavelet transform and support vector machines / Expert Systems with Applications 42(15-16):6075~6081
Y. Wu / 2016 / Feature extraction method of power quality disturbance signals based on modified S-transform / Proceedings of the Chinese Society of Electrical Engineering 36(10):2682~2689
F. Xu / 2012 / Classification for power quality short duration disturbances based on generalized S-transform / Proceedings of the Chinese Society of Electrical Engineering 32(4):77~84
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