원문정보
보안공학연구지원센터(IJGDC)
International Journal of Grid and Distributed Computing
Vol.9 No.5
2016.05
pp.69-78
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
Based on the second generation wavelet and information entropy, in this paper, we recognize the partial discharge pattern using the second generation wavelet (SGWT) and adaptive BP. Firstly, feature extraction of discharge signals are obtained using the SGWT and information entropy. Then, the extracted features are feed into the training BP network. The learning algorithm employed the conjugate gradient methods and the adaptive adjustment to train the error for BP network. Finally, we get the optimum training network, and the simulation results verified the feasibility of the algorithm.
목차
Abstract
1. Introductions
2. Feature Extraction based SGWT
2.1 Decomposition Process
2.2 Feature Extraction
2.3 Extraction Step
3. The BP Algorithm of Adaptive Adjustment
3.1 Standard BP Algorithm
3.2 Adaptive Adjustment Error
3.3 Conjugate Gradient Descent Method
4. Recognition Step
5. Simulation
6. Conclusion
References
1. Introductions
2. Feature Extraction based SGWT
2.1 Decomposition Process
2.2 Feature Extraction
2.3 Extraction Step
3. The BP Algorithm of Adaptive Adjustment
3.1 Standard BP Algorithm
3.2 Adaptive Adjustment Error
3.3 Conjugate Gradient Descent Method
4. Recognition Step
5. Simulation
6. Conclusion
References
저자정보
참고문헌
자료제공 : 네이버학술정보