Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSanchez, E.N.
dc.contributor.authorAlanis, A.Y.
dc.contributor.authorLoukianov, A.G.
dc.description.abstractThis chapter presents the design of an adaptive recurrent neural observer for nonlinear systems, whose mathematical model is assumed to be unknown. The observer is based on a recurrent high order neural network (RHONN), which estimates the state vector of the unknown plant dynamics and it has a Luenberger structure. The learning algorithm for the RHONN is implemented using an extended Kaiman filter (EKF). The respective stability analysis, on the basis of the Lyapunov approach, is included for the observer trained with an EKF and simulation results are included to illustrate the applicability of the proposed scheme. � 2008 Springer-Verlag Berlin Heidelberg.
dc.titleDiscrete-time neural observers
dc.relation.ispartofjournalStudies in Computational Intelligence
dc.contributor.affiliationSanchez, E.N., CINVESTAV, Unidad Guadalajara, Plaza la Luna, Guadalajara, Jalisco 45091, Mexico; Alan�s, A.Y., Departamento Deciencias Computacionales, CUCEI, Universidad de Guadalajara, Blvd. Marcelino Garcia Barragan 1421, Jalisco 44430, Mexico; Loukianov, A.G., CINVESTAV, Unidad Guadalajara, Plaza la Luna, Guadalajara, Jalisco 45091, Mexico
Appears in Collections:Producción científica UdeG

Files in This Item:
There are no files associated with this item.

Items in RIUdeG are protected by copyright, with all rights reserved, unless otherwise indicated.