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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.13 No.4
- 발행연도
- 2018.7
- 수록면
- 1,724 - 1,731 (8page)
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초록· 키워드
오류제보하기
The conventional polynomial neural network (PNN) is a classical flexible neural structure and self-organizing network, however it is not free from the limitation of overfitting problem. In this study, we propose a space search-optimized polynomial neural network (ssPNN) structure to alleviate this problem. Ranking selection is realized by means of ranking selection-based performance index (RS_PI) which is combined with conventional performance index (PI) and coefficients based performance index (CPI) (viz. the sum of squared coefficient). Unlike the conventional PNN, L2-norm regularization method for estimating the polynomial coefficients is also used when designing the ssPNN. Furthermore, space search optimization (SSO) is exploited here to optimize the parameters of ssPNN (viz. the number of input variables, which variables will be selected as input variables, and the type of polynomial). Experimental results show that the proposed ranking selection-based polynomial neural network gives rise to better performance in comparison with the neuron fuzzy models reported in the literatures.
목차
Abstract
1. Introduction
2. Motivation and Related Works
3. Design of Polynomial Neural Network Using Ranking Selection and L2 Norm Regularization
4. Space Search Optimization of Ranking Selection-based Polynomial Neural Network
5. Experimental Studies
6. Conclusions
References
참고문헌 (26)
참고문헌 신청
C. Riziotis and A.V. Vasilakos, “Computational intelligence in photonics technology and optical networks: a survey and future perspectives,” Information Sciences, vol. 177, pp. 5292-5315, 2007.
W. Huang and L. Ding, “Project-Scheduling problem with random time-dependent activity duration times,” IEEE Transactions on Engineering Management, vol. 58, pp. 377-387, 2011.
B. Wu, S. Han, J. Xiao, X. Hu, and J. Fan, “Error compensation based on BP neural network for airborne laser ranging,” International Journal for Light and Electron Optics vol. 127, pp. 4083-4088, 2016.
W. Huang, J. Wang, and J. Liao. “A granular classifier by means of context-based similarity clustering,” Journal of Electrical Engineering & Technology, vol. 11, pp. 1383-1394, 2016.
S. Yilmaz and Y. Oysal. “Fuzzy wavelet neural network models for prediction and identification of dynamical systems,” IEEE Transactions on Neural Networks, vol. 21, pp. 1599-1609, 2010.
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UCI(KEPA) : I410-ECN-0101-2018-560-002230828