TIIS (한국인터넷정보학회)
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| 한글제목(Korean Title) |
GA-based Feed-forward Self-organizing Neural Network Architecture and Its Applications for Multi-variable Nonlinear Process Systems |
| 영문제목(English Title) |
GA-based Feed-forward Self-organizing Neural Network Architecture and Its Applications for Multi-variable Nonlinear Process Systems |
| 저자(Author) |
Sung-Kwun Oh
Ho-Sung Park
Chang-Won Jeong
Su-Chong Joo
|
| 원문수록처(Citation) |
VOL 03 NO. 03 PP. 0309 ~ 0330 (2009. 06) |
한글내용
(Korean Abstract) |
|
영문내용
(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. |
| 키워드(Keyword) |
eed-forward self-organizing neural networks(FSONN)
genetic algorithms
group method of data handling(GMDH)
medical imaging system
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| 파일첨부 |
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