제어로봇시스템학회:학술대회논문집
- 제어로봇시스템학회 2003년도 ICCAS
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- Pages.156-161
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- 2003
Multi-dimensional extrapolation on use of multi multi-layer neural networks
- Oshige, Seisho (The Faculty of Engineering, Miyazaki University) ;
- Aoyama, Tomoo (The Faculty of Engineering, Miyazaki University) ;
- Nagashima, Umpei (Grid Technology Research Center, National Institute of Advanced Industrial Science and Technology)
- 발행 : 2003.10.22
초록
It is an interest problem to predict substance distributions in three-dimensional space. Recently, a research field as Geostatistics is advanced. It is a kind of inter- or extrapolation mathematically. Some useful means for the inter- and extrapolation are known, in which slide window method with neural networks is hopeful one. We propose multi-dimensional extrapolation using multi-layer neural networks and the slide-window method. The multi-dimensional extrapolation is not similar to one-dimension. It has plural algorithms. We researched line predictors and local-plain predictors I two-dimensional space. The both predictors are equivalent; however, in multi-dimensional extrapolation, it is very important to find the direction of predictions. Especially, since the slide window method requires information to predict the future in sampling data, if they are not ordered appropriately in the direction, the predictor cannot operate. We tested the extrapolation for typical two-dimensional functions, and found an excellent character of slide-window method based on local-plain. By using the method, we can extrapolate the function until twice-outer regions of the definitions.
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