DocumentCode
312837
Title
Adaptive prediction using fuzzy systems and neural networks
Author
Spooner, Jeffrey T. ; Passino, Kevin M.
Author_Institution
Control Subsyst. Dept., Sandia Nat. Labs., Albuquerque, NM, USA
Volume
2
fYear
1997
fDate
4-6 Jun 1997
Firstpage
1266
Abstract
A collection of adaptive prediction schemes which use the functional approximation properties of fuzzy systems is presented. Both direct and indirect approaches are developed using gradient and least squares update laws. It is proven that the prediction error converges asymptotically to zero for each case provided some minor assumptions hold
Keywords
discrete time systems; feedforward neural nets; function approximation; fuzzy systems; least squares approximations; multilayer perceptrons; neurocontrollers; prediction theory; adaptive prediction; direct approach; functional approximation; fuzzy systems; gradient laws; indirect approach; least squares update laws; neural networks; prediction error; Adaptive control; Biological neural networks; Control systems; Fuzzy sets; Fuzzy systems; Laboratories; Least squares approximation; Least squares methods; Neural networks; Programmable control;
fLanguage
English
Publisher
ieee
Conference_Titel
American Control Conference, 1997. Proceedings of the 1997
Conference_Location
Albuquerque, NM
ISSN
0743-1619
Print_ISBN
0-7803-3832-4
Type
conf
DOI
10.1109/ACC.1997.609738
Filename
609738
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