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
- 대한전기학회 Journal of Electrical Engineering & Technology Journal of Electrical Engineering & Technology Vol.7 No.1
- 발행연도
- 2012.1
- 수록면
- 109 - 114 (6page)
이용수
초록· 키워드
오류제보하기
In this paper, we utilize training strategy of hidden Markov model (HMM) to use in versatile issues such as classification of time-series sequential data such as electric transient disturbance problem in power system. For this, an automatic means of optimizing HMMs would be highly desirable, but it raises important issues: model interpretation and complexity control. With this in mind, we explore the possibility of using genetic algorithm (GA) and harmony search (HS) algorithm for optimizing the HMM. GA is flexible to allow incorporating other methods, such as Baum-Welch, within their cycle. Furthermore, operators that alter the structure of HMMs can be designed to simple structures. HS algorithm with parameter-setting free technique is proper for optimizing the parameters of HMM. HS algorithm is flexible so as to allow the elimination of requiring tedious parameter assigning efforts. In this paper, a sequential data analysis simulation is illustrated, and the optimized-HMMs are evaluated. The optimized HMM was capable of classifying a sequential data set for testing compared with the normal HMM.
목차
Abstract
1. Introduction
2. Overview of Related Works
3. HMM based Sequential Data Classification
4. Experimental Results
5. Conclusion
Acknowledgements
References
참고문헌 (18)
참고문헌 신청
EdwardJ.Powers / 1996 / Power Quality Disturbance Identification Using Wavelet Transforms and Artificial Neural Nets :615~618
EdwardJ.Powers / 2000 / Electric Power Transient Disturbance Classification Using Wavelet-Based Hidden Markov Models / IEEE 6:3662~3665
W. Min / 2004 / Classification of Power Quality Events using Optimal Time-Frequency Representations-Part 1: Theory / IEEE Transactions on Power Deliver 19(3):1488~1495
M. Negnevitsky / 2005 / A Neuro-Fuzzy System for Recognition of Power Quality Disturbances / IEEE Power Engineering Society General Meeting 3:2295~2300
M. C. G. Silveira / 2003 / Transient Stability Analysis of Electrical Power Systems using a Neural Network based on Fuzzy ARTMAP / Proc. IEEE Bologna PowerTech Conference
EdwardJ.Powers / 2000 / Electric Power Transient Disturbance Classification Using Wavelet-Based Hidden Markov Models / IEEE 6:3662~3665
W. Min / 2004 / Classification of Power Quality Events using Optimal Time-Frequency Representations-Part 1: Theory / IEEE Transactions on Power Deliver 19(3):1488~1495
M. Negnevitsky / 2005 / A Neuro-Fuzzy System for Recognition of Power Quality Disturbances / IEEE Power Engineering Society General Meeting 3:2295~2300
M. C. G. Silveira / 2003 / Transient Stability Analysis of Electrical Power Systems using a Neural Network based on Fuzzy ARTMAP / Proc. IEEE Bologna PowerTech Conference
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UCI(KEPA) : I410-ECN-0101-2013-560-001397457