TIIS (Çѱ¹ÀÎÅͳÝÁ¤º¸ÇÐȸ)
Current Result Document :
ÇѱÛÁ¦¸ñ(Korean Title) |
GA-based Feed-forward Self-organizing Neural Network Architecture and Its Applications for Multi-variable Nonlinear Process Systems |
¿µ¹®Á¦¸ñ(English Title) |
GA-based Feed-forward Self-organizing Neural Network Architecture and Its Applications for Multi-variable Nonlinear Process Systems |
ÀúÀÚ(Author) |
Sung-Kwun Oh
Ho-Sung Park
Chang-Won Jeong
Su-Chong Joo
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¿ø¹®¼ö·Ïó(Citation) |
VOL 03 NO. 03 PP. 0309 ~ 0330 (2009. 06) |
Çѱ۳»¿ë (Korean Abstract) |
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¿µ¹®³»¿ë (English Abstract) |
In this paper, we introduce the architecture of Genetic Algorithm(GA) based Feed-forward Polynomial Neural Networks(PNNs) and discuss a comprehensive design methodology. A conventional PNN consists of Polynomial Neurons, or nodes, located in several layers through a network growth process. In order to generate structurally optimized PNNs, a GA-based design procedure for each layer of the PNN leads to the selection of preferred nodes(PNs) with optimal parameters available within the PNN. To evaluate the performance of the GA-based PNN, experiments are done on a model by applying Medical Imaging System(MIS) data to a multi-variable software process. A comparative analysis shows that the proposed GA-based PNN is modeled with higher accuracy and more superb predictive capability than previously presented intelligent models. |
Å°¿öµå(Keyword) |
eed-forward self-organizing neural networks(FSONN)
genetic algorithms
group method of data handling(GMDH)
medical imaging system
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