Stabilization Analysis of Side-slope Based on Self-organizing Feature Map Neural Net

Author

Li, Ying ; Qie, Zhihong ; Wu, Xinmiao ; Zhang, Zhiyu

Author_Institution

Dept. of water conservancy Eng., Hebei Agric. Univ., Baoding

Volume

1

fYear

2006

fDate

16-18 Oct. 2006

Firstpage

37

Lastpage

41

Abstract

Self-organizing feature map (SOFM) was applied to analyze the stabilization of side-slope. A SOFM network model, which was trained and tested by the engineering examples, was established. The research shows that the SOFM presents excellent network performance, high prediction precision and is easy to run. As a result, the method is an effective way to evaluate the state of side-slope

Keywords

civil engineering computing; learning (artificial intelligence); mechanical stability; self-organising feature maps; SOFM network model; network performance; prediction precision; self-organizing feature map neural net; side-slope stabilization analysis; side-slope state evaluation; Agricultural engineering; Agriculture; Artificial neural networks; Biological neural networks; Biological system modeling; History; Neural networks; Neurons; Pattern recognition; Water conservation; Evaluation; Self-organizing Feature Map (SOFM); Side-slope; Stabilization;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems Design and Applications, 2006. ISDA '06. Sixth International Conference on

Conference_Location

Jinan

Print_ISBN

0-7695-2528-8

Type

conf

DOI

10.1109/ISDA.2006.249

Filename

4021405