대한전기학회:학술대회논문집 (Proceedings of the KIEE Conference)
- 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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- Pages.149-151
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- 2005
비선형 공정을 위한 최적 다항식 뉴럴네트워크에 관한 연구
A Study on Optimal Polynomial Neural Network for Nonlinear Process
- 발행 : 2005.10.28
초록
In this paper, we propose the Optimal Polynomial Neural Networks(PNN) for nonlinear process. The PNN 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. Medical Imaging System(MIS) data is simulated in order to confirm the efficiency and feasibility of the proposed approach in this paper.
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