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Title of Journal: Neural Comput Applic

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Abbravation: Neural Computing and Applications

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Springer London

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DOI

10.1007/bf01805379

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ISSN

1433-3058

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State estimation in MIMO nonlinear systems subject

Authors: J Humberto PérezCruz José de Jesús Rubio Jaime Pacheco Ezequiel Soriano
Publish Date: 2013/12/31
Volume: 25, Issue: 3-4, Pages: 693-701
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Abstract

This paper deals with the problem of state observation by means of a continuoustime recurrent neural network for a broad class of MIMO unknown nonlinear systems subject to unknown but bounded disturbances and with an unknown deadzone at each input With respect to previous works the main contribution of this study is twofold On the one hand the need of a matrix Riccati equation is conveniently avoided in this way the design process is considerably simplified On the other hand a faster convergence is carried out Specifically the exponential convergence of Euclidean norm of the observation error to a bounded zone is guaranteed Likewise the weights are shown to be bounded The main tool to prove these results is Lyapunovlike analysis A numerical example confirms the feasibility of our proposal

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