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Evaluation of Geotechnical Parameters Based on the Design of Optimal Neural Network Structure  

Park Hyun-Il (R&D Team, Samsung Corporation)
Hwang Dae-Jin (Dept. of Civil Engrg., University of Dong-Eui)
Kweon Gi-Chul (R&D Team, Samsung Corporation)
Lee Seung-Rae (Dept. of Civil &Envir. Engrg., KAIST)
Publication Information
Journal of the Korean Geotechnical Society / v.21, no.9, 2005 , pp. 25-34 More about this Journal
Abstract
This paper proposes a selection methodology composed of neural network (NN) and genetic algorithm (GA) to design optimal NN structure. We combine the characteristics of GA and NN to reduce the computational complexity of artificial intelligence applications and increase the precision of NN' prediction in the design of NN structure. Genetic selection approach of design parameters of NN is introduced to obtain optimal NN structure. Analyzed results for geotechnical problems are given to evaluate the performance of the proposed hybrid methodology.
Keywords
Artificial neural network; Genetic algorithm; Geotechnical parameter; Hybrid methodology;
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Times Cited By KSCI : 1  (Citation Analysis)
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