DocumentCode
1685510
Title
Physics without laws-making exact predictions with data based methods
Author
Kindermann, Lars ; Protzel, Peter
Author_Institution
Lab. for Math. Neurosci., RIKEN Brain Sci. Inst., Japan
Volume
2
fYear
2002
fDate
6/24/1905 12:00:00 AM
Firstpage
1673
Lastpage
1677
Abstract
The mathematical method of fractional or continuous iteration can be used to model a dynamical system exactly from limited experimental data. However, mathematics is complicated and exact solutions-even if proven to exist-can rarely be found analytically. We have shown previously that neural networks can be utilized to numerically compute fractional iterates of mathematical functions. In this paper we demonstrate the application of this method to the fundamental experiment of physics: the free fall
Keywords
iterative methods; multilayer perceptrons; physics; continuous iteration; data based methods; dynamical system; exact predictions; fractional iteration; free fall experiment; Brain modeling; Data mining; Equations; Mathematical model; Mathematics; Neuroscience; Physics; Predictive models; Training data; World Wide Web;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location
Honolulu, HI
ISSN
1098-7576
Print_ISBN
0-7803-7278-6
Type
conf
DOI
10.1109/IJCNN.2002.1007769
Filename
1007769
Link To Document