DocumentCode
2707368
Title
Stable fourier neural networks with application to modeling lettuce growth
Author
Cordova, Juan Jose ; Yu, Wen
Author_Institution
Dept. de Control Automatico, CINVESTAVIPN, Mexico City, Mexico
fYear
2009
fDate
14-19 June 2009
Firstpage
591
Lastpage
596
Abstract
In general, neural networks cannot match non-linear systems exactly. Neuro identifier has to include robust modification in order to guarantee Lyapunov stability. In this paper input-to-state stability approach is applied to access robust training algorithms of Fourier neural network (FoNN). It is successfully applied on modeling lettuce growth in green-house.
Keywords
Lyapunov methods; neural nets; nonlinear systems; stability; Lyapunov stability; green house; input-to-state stability; lettuce growth modeling; neuroidentifier; nonlinear systems; robust modification; robust training; stable Fourier neural network; Backpropagation algorithms; Greenhouses; Lyapunov method; Mathematical model; Neural networks; Nonlinear dynamical systems; Nonlinear systems; Robust stability; Robustness; Uncertainty;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2009. IJCNN 2009. International Joint Conference on
Conference_Location
Atlanta, GA
ISSN
1098-7576
Print_ISBN
978-1-4244-3548-7
Electronic_ISBN
1098-7576
Type
conf
DOI
10.1109/IJCNN.2009.5178671
Filename
5178671
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