Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.automatica.2005.07.003
Title: Further result on a dynamic recurrent neural-network-based adaptive observer for a class of nonlinear systems
Authors: Huang, S.N. 
Tan, K.K. 
Lee, T.H. 
Keywords: Adaptive control
Neural networks
Nonlinear systems
Observer
Issue Date: Dec-2005
Source: Huang, S.N., Tan, K.K., Lee, T.H. (2005-12). Further result on a dynamic recurrent neural-network-based adaptive observer for a class of nonlinear systems. Automatica 41 (12) : 2161-2162. ScholarBank@NUS Repository. https://doi.org/10.1016/j.automatica.2005.07.003
Abstract: In Kim et al. [(1997) A dynamic recurrent neural-network-based adaptive observer for a class of nonlinear systems. Automatica 33(8), 1539-1543], authors present an excellent neural network (NN) observer for a class of nonlinear systems. However, the output error equation in their paper is strictly positive real (SPR) which is restrictive assumption for nonlinear systems. In this note, by introducing a vector b0 and Lyapunov equation, the observer design is obtained without requiring the SPR condition. Thus, our observer can be applied to a wider class of systems. © 2005 Elsevier Ltd. All rights reserved.
Source Title: Automatica
URI: http://scholarbank.nus.edu.sg/handle/10635/56104
ISSN: 00051098
DOI: 10.1016/j.automatica.2005.07.003
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