대한전기학회:학술대회논문집 (Proceedings of the KIEE Conference)
- 대한전기학회 2005년도 제36회 하계학술대회 논문집 D
- /
- Pages.2876-2878
- /
- 2005
유전자 알고리즘 기반 최적 다항식 뉴럴네트워크 모델
Genetic Algorithms based Optimal Polynomial Neural Network Model
- Kim, Wan-Su (Department of Electrical Engineering, The University of Suwon) ;
-
Kim, Hyun-Ki
(Department of Electrical Engineering, The University of Suwon) ;
-
Oh, Sung-Kwun
(Department of Electrical Engineering, The University of Suwon)
- 발행 : 2005.07.18
초록
In this paper, we propose Genetic Algorithms(GAs)-based Optimal Polynomial Neural Networks(PNN). The proposed algorithm is based on Group Method of Data Handling(GMDH) method and its structure is similar to feedforward Neural Networks. But the structure of PNN is not fixed like in conventional Neural Networks and can be generated. The each node of PNN structure uses several types of high-order polynomial such as linear, quadratic and modified quadratic, and is connected as various kinds of multi-variable inputs. The conventional PNN depends on experience of a designer that select No. of input variable, input variable and polynomial type. Therefore it is very difficult a organizing of optimized network. The proposed algorithm identified and selected No. of input variable, input variable and polynomial type by using Genetic Algorithms(GAs). In the sequel the proposed model shows not only superior results to the existing models, but also pliability in organizing of optimal network. The study is illustrated with the ACI Distance Relay Data for application to power systems.
키워드